----------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\mariu\Box\myBox\Air Space Violations\Approval and Airspace Violations\Data\FPA Replication\FPA_ReplicationLog.log
  log type:  text
 opened on:  28 Dec 2020, 11:59:49

. 
. ****************************
. * Further Data Preparation *
. ****************************
. tsset modate
        time variable:  modate, 2002m1 to 2020m6
                delta:  1 month

. 
. * Interpolate small gaps in approval ratings and econ perceptions (three or less months)
. gen approval_ip=approval
(136 missing values generated)

. replace approval_ip=(l.approval+f.approval)/2 if approval==. & l.approval!=. & f.approval!=.
(1 real change made)

. 
. replace approval_ip=(l2.approval+1.5*f.approval)/2.5 if approval==. & l.approval==. & l2.approval!=. & f.approval!=.
(1 real change made)

. replace approval_ip=(1.5*l.approval+f2.approval)/2.5 if approval==. & f.approval==. & l.approval!=. & f2.approval!=.
(1 real change made)

. 
. replace approval_ip=(l2.approval+f2.approval)/2 if approval==. & l.approval==. & f.approval==. & l2.approval!=. & f2.approval!=.
(2 real changes made)

. replace approval_ip=(l3.approval+2*f.approval)/3 if approval==. & l.approval==. & l2.approval==. & l3.approval!=. & f.approval!=.
(2 real changes made)

. replace approval_ip=(2*l.approval+f3.approval)/3 if approval==. & f.approval==. & f2.approval==. & l.approval!=. & f3.approval!=.
(2 real changes made)

. 
. gen econ_improve_ip=econ_improve
(163 missing values generated)

. replace econ_improve_ip=(l.econ_improve+f.econ_improve)/2 if econ_improve==. & l.econ_improve!=. & f.econ_improve!=.
(2 real changes made)

. 
. replace econ_improve_ip=(l2.econ_improve+1.5*f.econ_improve)/2.5 if econ_improve==. & l.econ_improve==. & l2.econ_improve!=. & f.econ_improve!=.
(2 real changes made)

. replace econ_improve_ip=(1.5*l.econ_improve+f2.econ_improve)/2.5 if econ_improve==. & f.econ_improve==. & l.econ_improve!=. & f2.econ_improve!=.
(2 real changes made)

. 
. replace econ_improve_ip=(l2.econ_improve+f2.econ_improve)/2 if econ_improve==. & l.econ_improve==. & f.econ_improve==. & l2.econ_improve!=. & f2.e
> con_improve!=.
(0 real changes made)

. replace econ_improve_ip=(l3.econ_improve+2*f.econ_improve)/3 if econ_improve==. & l.econ_improve==. & l2.econ_improve==. & l3.econ_improve!=. & f.
> econ_improve!=.
(0 real changes made)

. replace econ_improve_ip=(2*l.econ_improve+f3.econ_improve)/3 if econ_improve==. & f.econ_improve==. & f2.econ_improve==. & l.econ_improve!=. & f3.
> econ_improve!=.
(0 real changes made)

. 
. * log Military Expenditures, GDP pc, and Violation counts
. gen ln_tumilex=ln(Tu_milex)
(6 missing values generated)

. gen ln_grmilex=ln(Gr_milex)
(6 missing values generated)

. gen ln_tugdppc=ln(trk_gdppc)
(6 missing values generated)

. 
. gen ln_airvio=ln(1+TotalAircrafts)
(84 missing values generated)

. gen airvio=TotalAircrafts
(84 missing values generated)

. 
. * Generate a dummy for Onset of Natural Gas Dispute
. gen gas_disp=0

. replace gas_disp=1 if modate>tm(2018m10)
(20 real changes made)

. 
. * Dummy indicators for domestic political events.
. * For turkish national elections, als includes constituional referendum
. gen tu_elec=0

. replace tu_elec=1 if modate==tm(2015m6) | modate==tm(2015m11) | modate==tm(2018m6) | ///
> modate==tm(2014m8) | modate==tm(2017m4)
(5 real changes made)

. 
. gen f_tu_elec=f.tu_elec
(1 missing value generated)

. replace f_tu_elec=0 if f_tu_elec==.
(1 real change made)

. 
. gen tu_coup=0

. replace tu_coup=1 if modate==tm(2016m7)
(1 real change made)

. 
. gen tu_gezi=0

. replace tu_gezi=1 if modate>=tm(2013m5) & modate<=tm(2013m8)
(4 real changes made)

. 
. gen tu_turmoil=0

. replace tu_turmoil=1 if modate>=tm(2013m5) & modate<=tm(2013m8)
(4 real changes made)

. replace tu_turmoil=1 if modate==tm(2016m7)
(1 real change made)

. 
. gen admin_no=.
(222 missing values generated)

. replace admin_no=1 if modate>=tm(2003m3) & modate<=tm(2007m8)
(54 real changes made)

. replace admin_no=2 if modate>=tm(2007m9) & modate<=tm(2011m6)
(46 real changes made)

. replace admin_no=3 if modate>=tm(2011m7) & modate<=tm(2014m8)
(38 real changes made)

. replace admin_no=4 if modate>=tm(2014m9) & modate<=tm(2018m6)
(46 real changes made)

. replace admin_no=5 if modate>tm(2018m6) & modate!=.
(24 real changes made)

. tab admin_no, gen(admin_fe)

   admin_no |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         54       25.96       25.96
          2 |         46       22.12       48.08
          3 |         38       18.27       66.35
          4 |         46       22.12       88.46
          5 |         24       11.54      100.00
------------+-----------------------------------
      Total |        208      100.00

. 
. * For greek national elections, als includes EU referendum
. gen gr_elec=0

. replace gr_elec=1 if modate==tm(2010m2) | modate==tm(2015m2) | modate==tm(2020m1) | ///
> modate==tm(2012m5) | modate==tm(2012m6) | modate==tm(2009m10) | modate==tm(2015m1) | ///
> modate==tm(2015m9)| modate==tm(2019m7)
(9 real changes made)

. 
. gen f_gr_elec=f.gr_elec
(1 missing value generated)

. replace f_gr_elec=0 if f_gr_elec==.
(1 real change made)

. 
. gen gr_euref=0

. replace gr_euref=1 if modate==tm(2015m7)
(1 real change made)

. gen f_gr_euref=f.gr_euref
(1 missing value generated)

. replace f_gr_euref=0 if f_gr_euref==.
(1 real change made)

. 
. label variable airvio "Airspace Violations"

. label variable approval_ip "Erdogan Approval"

. label variable econ_improve_ip "Econ perception: positive"

. 
. * Seasonality variables
. gen may=0

. replace may=1 if monthnum==5
(19 real changes made)

. 
. gen covid=0

. replace covid=1 if modate>tm(2020m2)
(4 real changes made)

. 
. 
. *********************************************
. * Time-series Diagnostics (see Appendix, A1) *
. *********************************************
. * Table A1
. dfuller ln_airvio if modate>tm(2013m8)

Dickey-Fuller test for unit root                   Number of obs   =        82

                               ---------- Interpolated Dickey-Fuller ---------
                  Test         1% Critical       5% Critical      10% Critical
               Statistic           Value             Value             Value
------------------------------------------------------------------------------
 Z(t)             -4.507            -3.535            -2.904            -2.587
------------------------------------------------------------------------------
MacKinnon approximate p-value for Z(t) = 0.0002

. pperron ln_airvio if modate>tm(2013m8)

Phillips-Perron test for unit root                 Number of obs   =        82
                                                   Newey-West lags =         3

                               ---------- Interpolated Dickey-Fuller ---------
                  Test         1% Critical       5% Critical      10% Critical
               Statistic           Value             Value             Value
------------------------------------------------------------------------------
 Z(rho)          -31.098           -19.476           -13.556           -10.892
 Z(t)             -4.414            -3.535            -2.904            -2.587
------------------------------------------------------------------------------
MacKinnon approximate p-value for Z(t) = 0.0003

. 
. * Figure A1
. bysort monthnum: egen volmean=mean(airvio)

. twoway (line airvio monthnum if Year==2009) (line airvio monthnum if Year==2010) ///
> (line airvio monthnum if Year==2011) (line airvio monthnum if Year==2012) ///
> (line airvio monthnum if Year==2013) (line airvio monthnum if Year==2014) ///
> (line airvio monthnum if Year==2015) (line airvio monthnum if Year==2016) ///
> (line airvio monthnum if Year==2017) (line airvio monthnum if Year==2018) ///
> (line airvio monthnum if Year==2019) (line airvio monthnum if Year==2020) ///
> (line volmean monthnum, lcolor(red)), legend(off) xscale(range(12)) scheme(plotplain) ///
> xtitle("Month") ytitle("Airspace Violations") xlabel(1(1)12, nogrid) ylab(, nogrid)

. 
. * Table A2
. tsset modate
        time variable:  modate, 2002m1 to 2020m6
                delta:  1 month

. 
. quietly arima ln_airvio l.approval_ip f_tu_elec gas_disp tu_coup admin_fe4 admin_fe5 if modate>tm(2013m8), ar(1 2) ma(1)

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |         81         .  -26.08862      11    74.17725   100.5162
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * AR2, MA1, May Dummy 
. quietly arima ln_airvio l.approval_ip may f_tu_elec gas_disp tu_coup admin_fe4 admin_fe5 if modate>tm(2013m8), ar(1 2) ma(1)

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |         81         .  -16.42331      12    56.84663   85.58002
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * AR3, MA1, May Dummy 
. quietly arima ln_airvio l.approval_ip may f_tu_elec gas_disp tu_coup admin_fe4 admin_fe5 if modate>tm(2013m8), ar(1 2 3) ma(1)

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |         81         .  -16.02631      13    58.05262   89.18046
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * AR3, MA2, May Dummy 
. quietly arima ln_airvio l.approval_ip may f_tu_elec gas_disp tu_coup admin_fe4 admin_fe5 if modate>tm(2013m8), ar(1 2 3) ma(1 2)

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |         81         .  -14.54305      13     55.0861   86.21394
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * AR2, MA2, May Dummy 
. quietly arima ln_airvio l.approval_ip may f_tu_elec gas_disp tu_coup admin_fe4 admin_fe5 if modate>tm(2013m8), ar(1 2) ma(1 2)

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |         81         .  -16.06614      12    56.13227   84.86566
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * AR2, MA3, May Dummy 
. quietly arima ln_airvio l.approval_ip may f_tu_elec gas_disp tu_coup admin_fe4 admin_fe5 if modate>tm(2013m8), ar(1 2) ma(1 2 3)

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |         81         .  -14.74139      14    57.48278   91.00507
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * AR1, MA1, May Dummy 
. quietly arima ln_airvio l.approval_ip may f_tu_elec gas_disp tu_coup admin_fe4 admin_fe5 if modate>tm(2013m8), ar(1) ma(1)

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |         81         .  -16.99752      11    55.99504   82.33398
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * AR1, MA2, May Dummy 
. quietly arima ln_airvio l.approval_ip may f_tu_elec gas_disp tu_coup admin_fe4 admin_fe5 if modate>tm(2013m8), ar(1) ma(1 2)

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |         81         .  -15.74204      12    55.48407   84.21746
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * AR1, MA3, May Dummy 
. quietly arima ln_airvio l.approval_ip may f_tu_elec gas_disp tu_coup admin_fe4 admin_fe5 if modate>tm(2013m8), ar(1) ma(1 2 3)

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |         81         .  -15.59497      13    57.18993   88.31777
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * AR3, MA3, May Dummy 
. quietly arima ln_airvio l.approval_ip may f_tu_elec gas_disp tu_coup admin_fe4 admin_fe5 if modate>tm(2013m8), ar(1 2 3) ma(1 2 3)

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |         81         .  -15.62423      15    61.24847    97.1652
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * AR1, May Dummy 
. quietly arima ln_airvio l.approval_ip may f_tu_elec gas_disp tu_coup admin_fe4 admin_fe5 if modate>tm(2013m8), ar(1)

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |         81         .  -17.06057      10    54.12115   78.06564
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * AR2, May Dummy 
. quietly arima ln_airvio l.approval_ip may f_tu_elec gas_disp tu_coup admin_fe4 admin_fe5 if modate>tm(2013m8), ar(1 2)

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |         81         .  -16.92321      11    55.84641   82.18535
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * AR3, May Dummy 
. quietly arima ln_airvio l.approval_ip may f_tu_elec gas_disp tu_coup admin_fe4 admin_fe5 if modate>tm(2013m8), ar(1 2 3)

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |         81         .  -16.03198      12    56.06396   84.79735
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * MA1, May Dummy 
. quietly arima ln_airvio l.approval_ip may f_tu_elec gas_disp tu_coup admin_fe4 admin_fe5 if modate>tm(2013m8), ma(1)

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |         81         .  -17.56893      10    55.13786   79.08235
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * MA2, May Dummy 
. quietly arima ln_airvio l.approval_ip may f_tu_elec gas_disp tu_coup admin_fe4 admin_fe5 if modate>tm(2013m8), ma(1 2)

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |         81         .  -16.53693      11    55.07386    81.4128
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * MA3, May Dummy 
. quietly arima ln_airvio l.approval_ip may f_tu_elec gas_disp tu_coup admin_fe4 admin_fe5 if modate>tm(2013m8), ma(1 2 3)

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |         81         .  -16.52506      12    57.05011    85.7835
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. 
. 
. *****************
. * ARMA Analysis *
. *****************
. 
. * Figure 1
. tsset modate
        time variable:  modate, 2002m1 to 2020m6
                delta:  1 month

. 
. twoway (line airvio modate) (line approval_ip modate, yaxis(2)) ///
> if modate>tm(2013m8), scheme(plotplain) legend(position(6)) xtitle("Month") ///
> ytitle("Airspace Violations", axis(1)) ytitle("Erdogan Approval", axis(2)) ///
> xlabel(#7, nogrid) ylab(, nogrid)

. 
. * Table 1
. sum ln_airvio l.approval_ip f_tu_elec tu_coup gas_disp l.econ_improve_ip  l12.ln_tumilex l12.ln_grmilex l12.ln_tugdppc if modate>tm(2013m8)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
   ln_airvio |         82    4.744191    .4790917   2.772589   5.793014
             |
 approval_ip |
         L1. |         81    45.89877    4.708324       37.5       67.6
             |
   f_tu_elec |         82    .0609756    .2407581          0          1
     tu_coup |         82    .0121951    .1104315          0          1
    gas_disp |         82    .2439024    .4320773          0          1
-------------+---------------------------------------------------------
             |
econ_impro~p |
         L1. |         65    33.53769    5.859352       23.5       45.9
             |
  ln_tumilex |
        L12. |         82    9.568863    .2044174   9.354996   9.942507
             |
  ln_grmilex |
        L12. |         82     8.57136    .0587566   8.492822   8.658192
             |
  ln_tugdppc |
        L12. |         82    9.882413    .0659216   9.735484   9.959947

. 
. * Table 2 (+ Appendix Table A3)
. arima ln_airvio l.approval_ip admin_fe4 admin_fe5 may if modate>tm(2013m8), ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood = -20.045376  
Iteration 1:   log likelihood = -19.827307  
Iteration 2:   log likelihood =  -19.68598  
Iteration 3:   log likelihood = -19.391674  
Iteration 4:   log likelihood = -19.382965  
(switching optimization to BFGS)
Iteration 5:   log likelihood = -19.340033  
Iteration 6:   log likelihood = -19.327196  
Iteration 7:   log likelihood = -19.322864  
Iteration 8:   log likelihood = -19.322603  
Iteration 9:   log likelihood = -19.322583  
Iteration 10:  log likelihood = -19.322582  

ARIMA regression

Sample:  2013m10 - 2020m6                       Number of obs     =         81
                                                Wald chi2(5)      =     188.07
Log likelihood = -19.32258                      Prob > chi2       =     0.0000

------------------------------------------------------------------------------
             |                 OPG
   ln_airvio |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ln_airvio    |
 approval_ip |
         L1. |  -.0241706   .0075444    -3.20   0.001    -.0389574   -.0093838
             |
   admin_fe4 |   .1217275   .1619667     0.75   0.452    -.1957214    .4391763
   admin_fe5 |   .6089785   .1875217     3.25   0.001     .2414427    .9765144
         may |   .5812604   .1153993     5.04   0.000     .3550819    .8074389
       _cons |   5.552038   .3696814    15.02   0.000     4.827476      6.2766
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |    .391578   .1325432     2.95   0.003     .1317981    .6513579
-------------+----------------------------------------------------------------
      /sigma |   .3068386   .0278486    11.02   0.000     .2522564    .3614208
------------------------------------------------------------------------------
Note: The test of the variance against zero is one sided, and the two-sided
      confidence interval is truncated at zero.

. estat aroots, nograph

   Eigenvalue stability condition
  +----------------------------------------+
  |        Eigenvalue        |   Modulus   |
  |--------------------------+-------------|
  |    .391578               |   .391578   |
  +----------------------------------------+
   All the eigenvalues lie inside the unit circle.
   AR parameters satisfy stability condition.

. arima ln_airvio l.approval_ip admin_fe4 admin_fe5 may f_tu_elec tu_coup gas_disp  if modate>tm(2013m8), ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood = -17.896205  
Iteration 1:   log likelihood = -17.688168  
Iteration 2:   log likelihood = -17.167863  
Iteration 3:   log likelihood = -17.082134  
Iteration 4:   log likelihood = -17.076134  
(switching optimization to BFGS)
Iteration 5:   log likelihood = -17.074906  
Iteration 6:   log likelihood = -17.066262  
Iteration 7:   log likelihood =  -17.06299  
Iteration 8:   log likelihood = -17.060937  
Iteration 9:   log likelihood = -17.060638  
Iteration 10:  log likelihood = -17.060578  
Iteration 11:  log likelihood = -17.060573  
Iteration 12:  log likelihood = -17.060573  

ARIMA regression

Sample:  2013m10 - 2020m6                       Number of obs     =         81
                                                Wald chi2(8)      =     158.84
Log likelihood = -17.06057                      Prob > chi2       =     0.0000

------------------------------------------------------------------------------
             |                 OPG
   ln_airvio |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ln_airvio    |
 approval_ip |
         L1. |  -.0283043   .0067909    -4.17   0.000    -.0416141   -.0149945
             |
   admin_fe4 |   .1005483   .1421824     0.71   0.479    -.1781241    .3792207
   admin_fe5 |   .5501484   .2509552     2.19   0.028     .0582853    1.042012
         may |   .5596038   .1249886     4.48   0.000     .3146306    .8045769
   f_tu_elec |   .1438931   .2699715     0.53   0.594    -.3852413    .6730274
     tu_coup |  -.6101426   .3083008    -1.98   0.048    -1.214401   -.0058842
    gas_disp |   .0779129   .2428007     0.32   0.748    -.3979677    .5537935
       _cons |   5.753362    .332619    17.30   0.000     5.101441    6.405283
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |    .279214   .1397391     2.00   0.046     .0053304    .5530977
-------------+----------------------------------------------------------------
      /sigma |   .2985537   .0252192    11.84   0.000      .249125    .3479825
------------------------------------------------------------------------------
Note: The test of the variance against zero is one sided, and the two-sided
      confidence interval is truncated at zero.

. estat aroots, nograph

   Eigenvalue stability condition
  +----------------------------------------+
  |        Eigenvalue        |   Modulus   |
  |--------------------------+-------------|
  |    .279214               |   .279214   |
  +----------------------------------------+
   All the eigenvalues lie inside the unit circle.
   AR parameters satisfy stability condition.

. arima ln_airvio l.approval_ip l.econ_improve_ip admin_fe4 admin_fe5 may f_tu_elec tu_coup gas_disp if modate>tm(2013m8), ar(1)
note: admin_fe5 dropped because of collinearity

(setting optimization to BHHH)
Iteration 0:   log likelihood = -14.423138  
Iteration 1:   log likelihood = -13.384528  
Iteration 2:   log likelihood =  -13.22869  
Iteration 3:   log likelihood = -12.982081  
Iteration 4:   log likelihood = -12.930515  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  -12.91662  
Iteration 6:   log likelihood = -12.893822  
Iteration 7:   log likelihood = -12.883822  
Iteration 8:   log likelihood = -12.881071  
Iteration 9:   log likelihood = -12.880296  
Iteration 10:  log likelihood = -12.880211  
Iteration 11:  log likelihood = -12.880205  
Iteration 12:  log likelihood = -12.880204  

ARIMA regression

Sample:  2015m2 - 2020m6                        Number of obs     =         65
                                                Wald chi2(8)      =     165.81
Log likelihood =  -12.8802                      Prob > chi2       =     0.0000

---------------------------------------------------------------------------------
                |                 OPG
      ln_airvio |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
ln_airvio       |
    approval_ip |
            L1. |  -.0330068   .0080457    -4.10   0.000    -.0487761   -.0172375
                |
econ_improve_ip |
            L1. |   .0162109   .0118301     1.37   0.171    -.0069757    .0393975
                |
      admin_fe4 |  -.5347712   .2091267    -2.56   0.011    -.9446521   -.1248903
            may |   .5545701    .120907     4.59   0.000     .3175968    .7915434
      f_tu_elec |   .1582498   .2687579     0.59   0.556    -.3685061    .6850056
        tu_coup |  -.5368357   .2864916    -1.87   0.061    -1.098349    .0246775
       gas_disp |   .1285843   .2108976     0.61   0.542    -.2847674     .541936
          _cons |    6.00089   .5360862    11.19   0.000      4.95018      7.0516
----------------+----------------------------------------------------------------
ARMA            |
             ar |
            L1. |    .405109   .1571159     2.58   0.010     .0971675    .7130504
----------------+----------------------------------------------------------------
         /sigma |   .2945912   .0303993     9.69   0.000     .2350098    .3541727
---------------------------------------------------------------------------------
Note: The test of the variance against zero is one sided, and the two-sided
      confidence interval is truncated at zero.

. estat aroots, nograph

   Eigenvalue stability condition
  +----------------------------------------+
  |        Eigenvalue        |   Modulus   |
  |--------------------------+-------------|
  |    .405109               |   .405109   |
  +----------------------------------------+
   All the eigenvalues lie inside the unit circle.
   AR parameters satisfy stability condition.

. arima ln_airvio l.approval_ip admin_fe4 admin_fe5 may f_tu_elec tu_coup gas_disp l12.ln_tumilex l12.ln_grmilex l12.ln_tugdppc if modate>tm(2013m8)
> , ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood = -15.791546  
Iteration 1:   log likelihood = -15.555371  
Iteration 2:   log likelihood = -15.420909  
Iteration 3:   log likelihood = -15.271192  
Iteration 4:   log likelihood = -15.234229  
(switching optimization to BFGS)
Iteration 5:   log likelihood = -15.229884  
Iteration 6:   log likelihood = -15.223694  
Iteration 7:   log likelihood = -15.220512  
Iteration 8:   log likelihood = -15.218764  
Iteration 9:   log likelihood = -15.218502  
Iteration 10:  log likelihood = -15.218028  
Iteration 11:  log likelihood = -15.217894  
Iteration 12:  log likelihood = -15.217817  
Iteration 13:  log likelihood = -15.217802  
Iteration 14:  log likelihood = -15.217799  
(switching optimization to BHHH)
Iteration 15:  log likelihood = -15.217798  

ARIMA regression

Sample:  2013m10 - 2020m6                       Number of obs     =         81
                                                Wald chi2(11)     =     189.30
Log likelihood =  -15.2178                      Prob > chi2       =     0.0000

------------------------------------------------------------------------------
             |                 OPG
   ln_airvio |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ln_airvio    |
 approval_ip |
         L1. |  -.0318756   .0073685    -4.33   0.000    -.0463176   -.0174336
             |
   admin_fe4 |  -.0004376   .1719134    -0.00   0.998    -.3373817    .3365065
   admin_fe5 |   .2944312   .3234031     0.91   0.363    -.3394273    .9282897
         may |   .5581012   .1401902     3.98   0.000     .2833335     .832869
   f_tu_elec |   .1289533   .2853294     0.45   0.651     -.430282    .6881886
     tu_coup |  -.6389253   .3911908    -1.63   0.102    -1.405645    .1277945
    gas_disp |  -.1527727   .3130758    -0.49   0.626    -.7663901    .4608446
             |
  ln_tumilex |
        L12. |   1.264474    1.11207     1.14   0.256    -.9151432    3.444092
             |
  ln_grmilex |
        L12. |   -1.04226   2.448219    -0.43   0.670    -5.840681    3.756162
             |
  ln_tugdppc |
        L12. |  -.1033098   2.142339    -0.05   0.962    -4.302217    4.095597
             |
       _cons |   3.960912   21.11109     0.19   0.851    -37.41607    45.33789
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .2376535   .1572729     1.51   0.131    -.0705958    .5459028
-------------+----------------------------------------------------------------
      /sigma |   .2918773   .0247453    11.80   0.000     .2433774    .3403771
------------------------------------------------------------------------------
Note: The test of the variance against zero is one sided, and the two-sided
      confidence interval is truncated at zero.

. estat aroots, nograph

   Eigenvalue stability condition
  +----------------------------------------+
  |        Eigenvalue        |   Modulus   |
  |--------------------------+-------------|
  |   .2376535               |   .237653   |
  +----------------------------------------+
   All the eigenvalues lie inside the unit circle.
   AR parameters satisfy stability condition.

. 
. 
. * Figure 2
. arima ln_airvio l.approval_ip admin_fe4 admin_fe5 may f_tu_elec tu_coup gas_disp l12.ln_tumilex l12.ln_grmilex l12.ln_tugdppc if modate>tm(2013m8)
> , ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood = -15.791546  
Iteration 1:   log likelihood = -15.555371  
Iteration 2:   log likelihood = -15.420909  
Iteration 3:   log likelihood = -15.271192  
Iteration 4:   log likelihood = -15.234229  
(switching optimization to BFGS)
Iteration 5:   log likelihood = -15.229884  
Iteration 6:   log likelihood = -15.223694  
Iteration 7:   log likelihood = -15.220512  
Iteration 8:   log likelihood = -15.218764  
Iteration 9:   log likelihood = -15.218502  
Iteration 10:  log likelihood = -15.218028  
Iteration 11:  log likelihood = -15.217894  
Iteration 12:  log likelihood = -15.217817  
Iteration 13:  log likelihood = -15.217802  
Iteration 14:  log likelihood = -15.217799  
(switching optimization to BHHH)
Iteration 15:  log likelihood = -15.217798  

ARIMA regression

Sample:  2013m10 - 2020m6                       Number of obs     =         81
                                                Wald chi2(11)     =     189.30
Log likelihood =  -15.2178                      Prob > chi2       =     0.0000

------------------------------------------------------------------------------
             |                 OPG
   ln_airvio |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ln_airvio    |
 approval_ip |
         L1. |  -.0318756   .0073685    -4.33   0.000    -.0463176   -.0174336
             |
   admin_fe4 |  -.0004376   .1719134    -0.00   0.998    -.3373817    .3365065
   admin_fe5 |   .2944312   .3234031     0.91   0.363    -.3394273    .9282897
         may |   .5581012   .1401902     3.98   0.000     .2833335     .832869
   f_tu_elec |   .1289533   .2853294     0.45   0.651     -.430282    .6881886
     tu_coup |  -.6389253   .3911908    -1.63   0.102    -1.405645    .1277945
    gas_disp |  -.1527727   .3130758    -0.49   0.626    -.7663901    .4608446
             |
  ln_tumilex |
        L12. |   1.264474    1.11207     1.14   0.256    -.9151432    3.444092
             |
  ln_grmilex |
        L12. |   -1.04226   2.448219    -0.43   0.670    -5.840681    3.756162
             |
  ln_tugdppc |
        L12. |  -.1033098   2.142339    -0.05   0.962    -4.302217    4.095597
             |
       _cons |   3.960912   21.11109     0.19   0.851    -37.41607    45.33789
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .2376535   .1572729     1.51   0.131    -.0705958    .5459028
-------------+----------------------------------------------------------------
      /sigma |   .2918773   .0247453    11.80   0.000     .2433774    .3403771
------------------------------------------------------------------------------
Note: The test of the variance against zero is one sided, and the two-sided
      confidence interval is truncated at zero.

. margins, dydx(l.approval_ip) level(90)

Average marginal effects                        Number of obs     =         81
Model VCE    : OPG

Expression   : xb prediction, one-step, predict()
dy/dx w.r.t. : L.approval_ip

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
 approval_ip |
         L1. |  -.0243938   .0090544    -2.69   0.007    -.0392869   -.0095007
------------------------------------------------------------------------------

. margins, at(l.approval_ip=(37.5(2.5)67.5)) level(90) 

Predictive margins                              Number of obs     =         81
Model VCE    : OPG

Expression   : xb prediction, one-step, predict()

1._at        : L.approval~p    =        37.5

2._at        : L.approval~p    =          40

3._at        : L.approval~p    =        42.5

4._at        : L.approval~p    =          45

5._at        : L.approval~p    =        47.5

6._at        : L.approval~p    =          50

7._at        : L.approval~p    =        52.5

8._at        : L.approval~p    =          55

9._at        : L.approval~p    =        57.5

10._at       : L.approval~p    =          60

11._at       : L.approval~p    =        62.5

12._at       : L.approval~p    =          65

13._at       : L.approval~p    =        67.5

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   4.949687   .0961303    51.49   0.000     4.791567    5.107807
          2  |   4.888703    .075754    64.53   0.000     4.764099    5.013307
          3  |   4.827718   .0571058    84.54   0.000     4.733788    4.921649
          4  |   4.766734   .0425234   112.10   0.000     4.696789    4.836679
          5  |   4.705749   .0371509   126.67   0.000     4.644642    4.766857
          6  |   4.644765   .0444623   104.47   0.000     4.571631    4.717899
          7  |    4.58378   .0599865    76.41   0.000     4.485111     4.68245
          8  |   4.522796    .079023    57.23   0.000     4.392815    4.652777
          9  |   4.461812   .0995774    44.81   0.000     4.298021    4.625602
         10  |   4.400827   .1208778    36.41   0.000     4.202001    4.599653
         11  |   4.339843   .1425904    30.44   0.000     4.105302    4.574383
         12  |   4.278858    .164552    26.00   0.000     4.008194    4.549522
         13  |   4.217874   .1866747    22.59   0.000     3.910821    4.524926
------------------------------------------------------------------------------

. marginsplot, scheme(plotplain) ciopts(recast(rline) lpattern(dash)) ytitle(ln Airspace Violations) xtitle(Approval Rate) ///
> legend(off) title(" ") text(5 38.2 "{it:141}") text(4.27 68.2 "{it:68}") text(4.68 50.7 "{it:104}") 

  Variables that uniquely identify margins: L.approval_ip

. 
. *********************************
. * VAR Diagnstotics and Analysis *
. *********************************
. 
. * Table A4
. dfuller approval_ip

Dickey-Fuller test for unit root                   Number of obs   =        86

                               ---------- Interpolated Dickey-Fuller ---------
                  Test         1% Critical       5% Critical      10% Critical
               Statistic           Value             Value             Value
------------------------------------------------------------------------------
 Z(t)             -3.326            -3.530            -2.901            -2.586
------------------------------------------------------------------------------
MacKinnon approximate p-value for Z(t) = 0.0138

. pperron approval_ip

Phillips-Perron test for unit root                 Number of obs   =        86
                                                   Newey-West lags =         3

                               ---------- Interpolated Dickey-Fuller ---------
                  Test         1% Critical       5% Critical      10% Critical
               Statistic           Value             Value             Value
------------------------------------------------------------------------------
 Z(rho)          -13.074           -19.548           -13.588           -10.916
 Z(t)             -2.979            -3.530            -2.901            -2.586
------------------------------------------------------------------------------
MacKinnon approximate p-value for Z(t) = 0.0369

. 
. * Table A5
. varsoc ln_airvio approval_ip if modate>tm(2013m8), exog(admin_fe4 admin_fe5 may f_tu_elec tu_coup gas_disp l12.ln_tumilex l12.ln_grmilex l12.ln_tu
> gdppc)

   Selection-order criteria
   Sample:  2014m1 - 2020m5                     Number of obs      =        77
  +---------------------------------------------------------------------------+
  |lag |    LL      LR      df    p      FPE       AIC      HQIC      SBIC    |
  |----+----------------------------------------------------------------------|
  |  0 | -221.733                      1.83305   6.27879   6.52229   6.88757  |
  |  1 | -205.505  32.456    4  0.000  1.33716   5.96118   6.25339*  6.69172* |
  |  2 | -203.799  3.4127    4  0.491  1.42358   6.02075   6.36166   6.87305  |
  |  3 | -196.671  14.255    4  0.007  1.31791   5.93952   6.32913   6.91357  |
  |  4 | -191.926  9.4914*   4  0.050  1.29954*  5.92015*  6.35846   7.01595  |
  +---------------------------------------------------------------------------+
   Endogenous:  ln_airvio approval_ip
    Exogenous:  admin_fe4 admin_fe5 may f_tu_elec tu_coup gas_disp
                L12.ln_tumilex L12.ln_grmilex L12.ln_tugdppc  _cons

. 
. * Table A6 and A7
. matrix A1 = (1,0\ .,1)

. matrix B1 = (.,0 \ 0,.)

. 
. svar ln_airvio approval_ip if modate>tm(2013m8), exog(admin_fe4 admin_fe5 may f_tu_elec tu_coup gas_disp l12.ln_tumilex l12.ln_grmilex l12.ln_tugd
> ppc) ///
> lags(1) aeq(A1) beq(B1) var

Vector autoregression

Sample:  2013m10 - 2020m5                       Number of obs     =         80
Log likelihood =  -211.8886                     AIC               =   5.897214
FPE            =   1.253603                     HQIC              =   6.183721
Det(Sigma_ml)  =   .6848604                     SBIC              =   6.611822

Equation           Parms      RMSE     R-sq      chi2     P>chi2
----------------------------------------------------------------
ln_airvio            12     .315985   0.6346   138.9227   0.0000
approval_ip          12     3.13383   0.6224   131.8606   0.0000
----------------------------------------------------------------

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ln_airvio    |
   ln_airvio |
         L1. |   .1897679   .0970644     1.96   0.051    -.0004748    .3800106
             |
 approval_ip |
         L1. |  -.0284231   .0093073    -3.05   0.002     -.046665   -.0101811
             |
   admin_fe4 |  -.0028288    .141867    -0.02   0.984     -.280883    .2752255
   admin_fe5 |   .3025964   .2479889     1.22   0.222    -.1834529    .7886458
         may |   .5557465   .1241018     4.48   0.000     .3125114    .7989816
   f_tu_elec |   .1542638   .1456371     1.06   0.289    -.1311796    .4397072
     tu_coup |  -.9119225    .303158    -3.01   0.003    -1.506101   -.3177437
    gas_disp |  -.1697917   .2058924    -0.82   0.410    -.5733334      .23375
             |
  ln_tumilex |
        L12. |   .7573055   .8017132     0.94   0.345    -.8140235    2.328635
             |
  ln_grmilex |
        L12. |   .0229253   1.798637     0.01   0.990    -3.502338    3.548188
             |
  ln_tugdppc |
        L12. |  -.1033197   1.566144    -0.07   0.947    -3.172906    2.966267
             |
       _cons |  -1.365894   15.06481    -0.09   0.928    -30.89238    28.16059
-------------+----------------------------------------------------------------
approval_ip  |
   ln_airvio |
         L1. |  -.7248289   .9626499    -0.75   0.451    -2.611588     1.16193
             |
 approval_ip |
         L1. |   .3830308   .0923063     4.15   0.000     .2021137    .5639478
             |
   admin_fe4 |  -1.611283   1.406986    -1.15   0.252    -4.368925     1.14636
   admin_fe5 |  -4.030119   2.459466    -1.64   0.101    -8.850583    .7903453
         may |  -.6472647   1.230798    -0.53   0.599    -3.059584    1.765054
   f_tu_elec |  -1.098664   1.444376    -0.76   0.447     -3.92959    1.732261
     tu_coup |   21.24106   3.006613     7.06   0.000      15.3482    27.13391
    gas_disp |  -.8549833   2.041968    -0.42   0.675    -4.857166      3.1472
             |
  ln_tumilex |
        L12. |   1.501343   7.951105     0.19   0.850    -14.08254    17.08522
             |
  ln_grmilex |
        L12. |   31.60938   17.83824     1.77   0.076    -3.352917    66.57168
             |
  ln_tugdppc |
        L12. |   2.228522   15.53246     0.14   0.886    -28.21454    32.67158
             |
       _cons |  -273.3925   149.4074    -1.83   0.067    -566.2256    19.44067
------------------------------------------------------------------------------
Estimating short-run parameters

Iteration 0:   log likelihood = -438.72187  
Iteration 1:   log likelihood = -240.64943  
Iteration 2:   log likelihood = -212.41497  
Iteration 3:   log likelihood = -211.88925  
Iteration 4:   log likelihood = -211.88855  
Iteration 5:   log likelihood = -211.88855  

Structural vector autoregression

 ( 1)  [a_1_1]_cons = 1
 ( 2)  [a_1_2]_cons = 0
 ( 3)  [a_2_2]_cons = 1
 ( 4)  [b_1_2]_cons = 0
 ( 5)  [b_2_1]_cons = 0

Sample:  2013m10 - 2020m5                       Number of obs     =         80
Exactly identified model                        Log likelihood    =  -211.8886

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      /a_1_1 |          1  (constrained)
      /a_2_1 |   1.810495   1.090194     1.66   0.097    -.3262454    3.947235
      /a_1_2 |          0  (constrained)
      /a_2_2 |          1  (constrained)
-------------+----------------------------------------------------------------
      /b_1_1 |    .291324   .0230312    12.65   0.000     .2461837    .3364643
      /b_2_1 |          0  (constrained)
      /b_1_2 |          0  (constrained)
      /b_2_2 |   2.840696   .2245768    12.65   0.000     2.400534    3.280859
------------------------------------------------------------------------------

. 
. vargranger

   Granger causality Wald tests
  +------------------------------------------------------------------+
  |          Equation           Excluded |   chi2     df Prob > chi2 |
  |--------------------------------------+---------------------------|
  |         ln_airvio        approval_ip |   9.326     1    0.002    |
  |         ln_airvio                ALL |   9.326     1    0.002    |
  |--------------------------------------+---------------------------|
  |       approval_ip          ln_airvio |  .56694     1    0.451    |
  |       approval_ip                ALL |  .56694     1    0.451    |
  +------------------------------------------------------------------+

. varstable

   Eigenvalue stability condition
  +----------------------------------------+
  |        Eigenvalue        |   Modulus   |
  |--------------------------+-------------|
  |   .4594297               |    .45943   |
  |    .113369               |   .113369   |
  +----------------------------------------+
   All the eigenvalues lie inside the unit circle.
   VAR satisfies stability condition.

. 
. * Figure 3
. cd "C:\Users\mariu\Documents"
C:\Users\mariu\Documents

. irf create order1, set(var2.irf) replace step(12)
(file var2.irf now active)
(file var2.irf updated)

. 
. irf graph sirf, xlabel(0(2)12) irf(order1) impulse(approval_ip) response(ln_airvio) ///
> yline(0,lcolor(black)) scheme(plotplain) level(90) byopts(note("") legend(off)) ///
> subtitle(" ", fcolor(none) lstyle(none)) xtitle("Months after Approval Shock")  ///
> name(one, replace) ytitle("Airspace Violations Response")

. irf graph sirf, xlabel(0(2)12) irf(order1) impulse(ln_airvio) response(approval_ip) ///
> yline(0,lcolor(black)) scheme(plotplain) level(90) byopts(note("") legend(off)) ///
> subtitle(" ", fcolor(none) lstyle(none)) xtitle("Months after Airspace Violations Shock")  ///
> name(two, replace) ytitle("Approval Response")

. graph combine one two, ycommon scheme(plotplain)

. 
. *********************
. * Robustness Checks *
. *********************
. 
. * Table A8 - No interpolation in Approval
. arima ln_airvio l.approval admin_fe4 admin_fe5 may if modate>tm(2013m8), ar(1)

Number of gaps in sample:  3
(note: filtering over missing observations)

(setting optimization to BHHH)
Iteration 0:   log likelihood = -18.768832  
Iteration 1:   log likelihood = -18.699791  
Iteration 2:   log likelihood = -18.345983  
Iteration 3:   log likelihood = -18.002602  
Iteration 4:   log likelihood = -17.904432  
(switching optimization to BFGS)
Iteration 5:   log likelihood = -17.888275  
Iteration 6:   log likelihood = -17.875079  
Iteration 7:   log likelihood = -17.871579  
Iteration 8:   log likelihood = -17.871006  
Iteration 9:   log likelihood = -17.870977  
Iteration 10:  log likelihood = -17.870974  
Iteration 11:  log likelihood = -17.870974  

ARIMA regression

Sample:  2013m10 - 2020m6, but with gaps        Number of obs     =         75
                                                Wald chi2(5)      =     190.21
Log likelihood = -17.87097                      Prob > chi2       =     0.0000

------------------------------------------------------------------------------
             |                 OPG
   ln_airvio |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ln_airvio    |
    approval |
         L1. |   -.024144   .0078509    -3.08   0.002    -.0395315   -.0087565
             |
   admin_fe4 |   .0487966   .1828939     0.27   0.790    -.3096688     .407262
   admin_fe5 |   .5333558     .19347     2.76   0.006     .1541616    .9125501
         may |   .5756308   .1140584     5.05   0.000     .3520804    .7991813
       _cons |   5.621065    .392958    14.30   0.000     4.850882    6.391249
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .4198922   .1392958     3.01   0.003     .1468775    .6929068
-------------+----------------------------------------------------------------
      /sigma |   .3055697   .0294869    10.36   0.000     .2477765    .3633629
------------------------------------------------------------------------------
Note: The test of the variance against zero is one sided, and the two-sided
      confidence interval is truncated at zero.

. arima ln_airvio l.approval admin_fe4 admin_fe5 may f_tu_elec tu_coup gas_disp if modate>tm(2013m8), ar(1)

Number of gaps in sample:  3
(note: filtering over missing observations)

(setting optimization to BHHH)
Iteration 0:   log likelihood = -16.942298  
Iteration 1:   log likelihood = -16.393969  
Iteration 2:   log likelihood =  -16.07169  
Iteration 3:   log likelihood = -15.934127  
Iteration 4:   log likelihood = -15.927316  
(switching optimization to BFGS)
Iteration 5:   log likelihood = -15.925514  
Iteration 6:   log likelihood = -15.917485  
Iteration 7:   log likelihood = -15.914394  
Iteration 8:   log likelihood = -15.912616  
Iteration 9:   log likelihood = -15.912125  
Iteration 10:  log likelihood = -15.912093  
Iteration 11:  log likelihood = -15.912076  
Iteration 12:  log likelihood = -15.912075  

ARIMA regression

Sample:  2013m10 - 2020m6, but with gaps        Number of obs     =         75
                                                Wald chi2(8)      =     160.84
Log likelihood = -15.91208                      Prob > chi2       =     0.0000

------------------------------------------------------------------------------
             |                 OPG
   ln_airvio |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ln_airvio    |
    approval |
         L1. |  -.0286671   .0075508    -3.80   0.000    -.0434664   -.0138678
             |
   admin_fe4 |    .058184   .1594094     0.36   0.715    -.2542527    .3706207
   admin_fe5 |   .4995241   .2527019     1.98   0.048     .0042375    .9948107
         may |   .5557485   .1252812     4.44   0.000     .3102019    .8012951
   f_tu_elec |   .1312844   .2735928     0.48   0.631    -.4049477    .6675165
     tu_coup |  -.5618538   .2864043    -1.96   0.050    -1.123196   -.0005117
    gas_disp |   .0821444   .2427115     0.34   0.735    -.3935614    .5578503
       _cons |   5.813235    .371651    15.64   0.000     5.084812    6.541657
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .3159812   .1528089     2.07   0.039     .0164812    .6154812
-------------+----------------------------------------------------------------
      /sigma |   .2983416   .0265578    11.23   0.000     .2462891     .350394
------------------------------------------------------------------------------
Note: The test of the variance against zero is one sided, and the two-sided
      confidence interval is truncated at zero.

. arima ln_airvio l.approval l.econ_improve_ip admin_fe4 admin_fe5 may f_tu_elec tu_coup gas_disp if modate>tm(2013m8), ar(1)
note: admin_fe5 dropped because of collinearity

Number of gaps in sample:  1
(note: filtering over missing observations)

(setting optimization to BHHH)
Iteration 0:   log likelihood = -13.981783  
Iteration 1:   log likelihood =  -13.14649  
Iteration 2:   log likelihood = -12.827761  
Iteration 3:   log likelihood = -12.663496  
Iteration 4:   log likelihood = -12.627822  
(switching optimization to BFGS)
Iteration 5:   log likelihood = -12.618397  
Iteration 6:   log likelihood = -12.607598  
Iteration 7:   log likelihood = -12.603521  
Iteration 8:   log likelihood = -12.602292  
Iteration 9:   log likelihood = -12.601786  
Iteration 10:  log likelihood = -12.601656  
Iteration 11:  log likelihood = -12.601648  
Iteration 12:  log likelihood = -12.601646  
Iteration 13:  log likelihood = -12.601646  

ARIMA regression

Sample:  2015m2 - 2020m6, but with a gap        Number of obs     =         64
                                                Wald chi2(8)      =     166.01
Log likelihood = -12.60165                      Prob > chi2       =     0.0000

---------------------------------------------------------------------------------
                |                 OPG
      ln_airvio |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
ln_airvio       |
       approval |
            L1. |  -.0339543   .0080628    -4.21   0.000    -.0497571   -.0181515
                |
econ_improve_ip |
            L1. |   .0150772    .012005     1.26   0.209    -.0084521    .0386066
                |
      admin_fe4 |  -.5265252   .2117154    -2.49   0.013    -.9414797   -.1115706
            may |   .5549403   .1214831     4.57   0.000     .3168378    .7930428
      f_tu_elec |   .1546765   .2689779     0.58   0.565    -.3725105    .6818635
        tu_coup |  -.5577536   .2937947    -1.90   0.058    -1.133581    .0180733
       gas_disp |   .1217959   .2113124     0.58   0.564    -.2923687    .5359606
          _cons |   6.084878   .5619842    10.83   0.000      4.98341    7.186347
----------------+----------------------------------------------------------------
ARMA            |
             ar |
            L1. |   .3885695   .1562939     2.49   0.013     .0822392    .6948999
----------------+----------------------------------------------------------------
         /sigma |   .2939285   .0302238     9.73   0.000     .2346909    .3531661
---------------------------------------------------------------------------------
Note: The test of the variance against zero is one sided, and the two-sided
      confidence interval is truncated at zero.

. arima ln_airvio l.approval admin_fe4 admin_fe5 may f_tu_elec tu_coup gas_disp l12.ln_tumilex l12.ln_grmilex l12.ln_tugdppc if modate>tm(2013m8), a
> r(1)

Number of gaps in sample:  3
(note: filtering over missing observations)

(setting optimization to BHHH)
Iteration 0:   log likelihood = -14.682555  
Iteration 1:   log likelihood = -14.318058  
Iteration 2:   log likelihood =  -14.30938  
Iteration 3:   log likelihood = -14.082772  
Iteration 4:   log likelihood = -14.059026  
(switching optimization to BFGS)
Iteration 5:   log likelihood = -14.015098  
Iteration 6:   log likelihood = -14.009607  
Iteration 7:   log likelihood = -14.003191  
Iteration 8:   log likelihood = -13.999081  
Iteration 9:   log likelihood = -13.996183  
Iteration 10:  log likelihood = -13.995482  
Iteration 11:  log likelihood = -13.995031  
Iteration 12:  log likelihood = -13.994908  
Iteration 13:  log likelihood = -13.994874  
Iteration 14:  log likelihood = -13.994866  
(switching optimization to BHHH)
Iteration 15:  log likelihood = -13.994866  

ARIMA regression

Sample:  2013m10 - 2020m6, but with gaps        Number of obs     =         75
                                                Wald chi2(11)     =     186.34
Log likelihood = -13.99487                      Prob > chi2       =     0.0000

------------------------------------------------------------------------------
             |                 OPG
   ln_airvio |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ln_airvio    |
    approval |
         L1. |   -.032921   .0078424    -4.20   0.000    -.0482918   -.0175502
             |
   admin_fe4 |  -.0809518   .2262149    -0.36   0.720    -.5243248    .3624212
   admin_fe5 |   .1823441    .379925     0.48   0.631    -.5622953    .9269835
         may |   .5564754   .1399572     3.98   0.000     .2821644    .8307864
   f_tu_elec |   .1174523   .3047352     0.39   0.700    -.4798176    .7147222
     tu_coup |  -.6074702   .3631871    -1.67   0.094    -1.319304    .1043634
    gas_disp |  -.1358206   .3329121    -0.41   0.683    -.7883163    .5166751
             |
  ln_tumilex |
        L12. |   1.197679   1.265016     0.95   0.344    -1.281707    3.677065
             |
  ln_grmilex |
        L12. |  -1.214614    3.78622    -0.32   0.748    -8.635469    6.206242
             |
  ln_tugdppc |
        L12. |   .6073033    3.22023     0.19   0.850    -5.704232    6.918838
             |
       _cons |  -.8230058   26.53953    -0.03   0.975    -52.83952    51.19351
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .2665075   .1696815     1.57   0.116    -.0660623    .5990772
-------------+----------------------------------------------------------------
      /sigma |    .291051     .02567    11.34   0.000     .2407387    .3413633
------------------------------------------------------------------------------
Note: The test of the variance against zero is one sided, and the two-sided
      confidence interval is truncated at zero.

. 
. * Table A9 - * Coup control and structural break
. list modate if tu_coup==1

     +--------+
     | modate |
     |--------|
175. | 2016m7 |
     +--------+

. gen postcoupat=0

. replace postcoupat=1 if modate>=tm(2016m7)
(48 real changes made)

. gen appXcoup=l.approval_ip*postcoupat
(127 missing values generated)

. arima ln_airvio l.approval_ip postcoupat admin_fe4 admin_fe5 may f_tu_elec gas_disp l12.ln_tumilex l12.ln_grmilex l12.ln_tugdppc if modate>tm(2013
> m8), ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood = -17.828442  
Iteration 1:   log likelihood = -17.653868  
Iteration 2:   log likelihood = -16.771385  
Iteration 3:   log likelihood = -16.723541  
Iteration 4:   log likelihood = -16.647501  
(switching optimization to BFGS)
Iteration 5:   log likelihood = -16.629951  
Iteration 6:   log likelihood = -16.602873  
Iteration 7:   log likelihood = -16.595224  
Iteration 8:   log likelihood = -16.592423  
Iteration 9:   log likelihood = -16.591581  
Iteration 10:  log likelihood = -16.591132  
Iteration 11:  log likelihood = -16.590943  
Iteration 12:  log likelihood = -16.590913  
Iteration 13:  log likelihood =  -16.59091  
Iteration 14:  log likelihood =  -16.59091  

ARIMA regression

Sample:  2013m10 - 2020m6                       Number of obs     =         81
                                                Wald chi2(11)     =     190.25
Log likelihood = -16.59091                      Prob > chi2       =     0.0000

------------------------------------------------------------------------------
             |                 OPG
   ln_airvio |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ln_airvio    |
 approval_ip |
         L1. |  -.0224484   .0074592    -3.01   0.003    -.0370683   -.0078286
             |
  postcoupat |  -.1836592   .1755717    -1.05   0.296    -.5277735    .1604551
   admin_fe4 |   .0301904   .1966368     0.15   0.878    -.3552107    .4155915
   admin_fe5 |   .2699646   .3479328     0.78   0.438    -.4119712    .9519003
         may |   .5248059   .1328141     3.95   0.000     .2644951    .7851166
   f_tu_elec |   .1348181   .2924824     0.46   0.645    -.4384369    .7080731
    gas_disp |  -.1520277   .3530906    -0.43   0.667    -.8440726    .5400171
             |
  ln_tumilex |
        L12. |   1.547446   1.329027     1.16   0.244    -1.057398    4.152291
             |
  ln_grmilex |
        L12. |  -1.430141   2.879499    -0.50   0.619    -7.073856    4.213575
             |
  ln_tugdppc |
        L12. |   .7030926   2.699697     0.26   0.795    -4.588216    5.994401
             |
       _cons |  -3.734218   30.78095    -0.12   0.903    -64.06376    56.59533
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .3874318   .1425896     2.72   0.007     .1079613    .6669023
-------------+----------------------------------------------------------------
      /sigma |   .2966763   .0275544    10.77   0.000     .2426707     .350682
------------------------------------------------------------------------------
Note: The test of the variance against zero is one sided, and the two-sided
      confidence interval is truncated at zero.

. arima ln_airvio l.approval_ip postcoupat appXcoup admin_fe4 admin_fe5 may f_tu_elec gas_disp l12.ln_tumilex l12.ln_grmilex l12.ln_tugdppc if modat
> e>tm(2013m8), ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood = -16.093068  
Iteration 1:   log likelihood = -15.163809  
Iteration 2:   log likelihood = -15.072867  
Iteration 3:   log likelihood = -14.969856  
Iteration 4:   log likelihood = -14.448397  
(switching optimization to BFGS)
Iteration 5:   log likelihood = -14.391463  
Iteration 6:   log likelihood = -14.306721  
Iteration 7:   log likelihood = -14.291094  
Iteration 8:   log likelihood = -14.288175  
Iteration 9:   log likelihood = -14.287936  
Iteration 10:  log likelihood = -14.287236  
Iteration 11:  log likelihood =  -14.28713  
Iteration 12:  log likelihood =  -14.28705  
Iteration 13:  log likelihood = -14.287035  
Iteration 14:  log likelihood = -14.287034  
(switching optimization to BHHH)
Iteration 15:  log likelihood = -14.287033  

ARIMA regression

Sample:  2013m10 - 2020m6                       Number of obs     =         81
                                                Wald chi2(12)     =     186.23
Log likelihood = -14.28703                      Prob > chi2       =     0.0000

------------------------------------------------------------------------------
             |                 OPG
   ln_airvio |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ln_airvio    |
 approval_ip |
         L1. |   .0181848   .0228434     0.80   0.426    -.0265874    .0629571
             |
  postcoupat |    2.00786   1.018256     1.97   0.049     .0121152    4.003605
    appXcoup |  -.0492015    .022751    -2.16   0.031    -.0937927   -.0046103
   admin_fe4 |   .1566445   .2400619     0.65   0.514    -.3138682    .6271572
   admin_fe5 |   .3566879   .3832702     0.93   0.352    -.3945079    1.107884
         may |   .4910512   .1155962     4.25   0.000     .2644867    .7176156
   f_tu_elec |   .1789177   .2215369     0.81   0.419    -.2552865     .613122
    gas_disp |  -.1492873    .363817    -0.41   0.682    -.8623555    .5637809
             |
  ln_tumilex |
        L12. |   1.842887   1.358204     1.36   0.175    -.8191433    4.504917
             |
  ln_grmilex |
        L12. |  -2.886365   2.902266    -0.99   0.320    -8.574701    2.801971
             |
  ln_tugdppc |
        L12. |   .8325799   2.891345     0.29   0.773    -4.834352    6.499512
             |
       _cons |   2.761395   32.52029     0.08   0.932     -60.9772    66.49999
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .4189755   .1490017     2.81   0.005     .1269376    .7110134
-------------+----------------------------------------------------------------
      /sigma |   .2883024    .031297     9.21   0.000     .2269614    .3496435
------------------------------------------------------------------------------
Note: The test of the variance against zero is one sided, and the two-sided
      confidence interval is truncated at zero.

. 
. * Tables A10+A11 - Further political events
. gen deadlyearthquake=0

. replace deadlyearthquake=1 if modate==tm(2017m7) | modate==tm(2017m6) | modate==tm(2015m11) ///
> | modate==tm(2014m5) | modate==tm(2020m6) | modate==tm(2020m2) | modate==tm(2020m1) | modate==tm(2019m9)
(8 real changes made)

. gen visit = 0

. replace visit=1 if modate==tm(2017m12) | modate==tm(2019m2)
(2 real changes made)

. gen davut_exit=0

. replace davut_exit=1 if modate>=tm(2016m5)
(50 real changes made)

. gen natosummit=0

. replace natosummit=1 if modate==tm(2010m11) | modate==tm(2012m5) | modate==tm(2014m9) | modate==tm(2016m7) ///
> | modate==tm(2017m5) | modate==tm(2018m7) | modate==tm(2019m12)
(7 real changes made)

. gen syria_op=0

. replace syria_op=1 if modate==tm(2019m11) | modate==tm(2019m10)
(2 real changes made)

. replace syria_op=1 if modate>=tm(2018m1) & modate<=tm(2018m3)
(3 real changes made)

. replace syria_op=1 if modate>=tm(2016m8) & modate<=tm(2017m3)
(8 real changes made)

. 
. arima ln_airvio l.approval_ip f_gr_elec admin_fe4 admin_fe5 may f_tu_elec tu_coup gas_disp l12.ln_tumilex l12.ln_grmilex l12.ln_tugdppc if modate>
> tm(2013m8), ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood = -15.783531  
Iteration 1:   log likelihood = -15.537129  
Iteration 2:   log likelihood = -15.403114  
Iteration 3:   log likelihood = -15.241855  
Iteration 4:   log likelihood = -15.200548  
(switching optimization to BFGS)
Iteration 5:   log likelihood = -15.190058  
Iteration 6:   log likelihood = -15.188474  
Iteration 7:   log likelihood = -15.185747  
Iteration 8:   log likelihood = -15.185018  
Iteration 9:   log likelihood = -15.184355  
Iteration 10:  log likelihood = -15.184174  
Iteration 11:  log likelihood = -15.184009  
Iteration 12:  log likelihood = -15.183994  
Iteration 13:  log likelihood = -15.183991  
Iteration 14:  log likelihood = -15.183991  

ARIMA regression

Sample:  2013m10 - 2020m6                       Number of obs     =         81
                                                Wald chi2(12)     =     189.07
Log likelihood = -15.18399                      Prob > chi2       =     0.0000

------------------------------------------------------------------------------
             |                 OPG
   ln_airvio |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ln_airvio    |
 approval_ip |
         L1. |  -.0316302   .0074202    -4.26   0.000    -.0461734   -.0170869
             |
   f_gr_elec |  -.0364583   .1338999    -0.27   0.785    -.2988973    .2259808
   admin_fe4 |   .0064591   .1810189     0.04   0.972    -.3483314    .3612496
   admin_fe5 |   .3011636   .3283045     0.92   0.359    -.3423014    .9446287
         may |   .5548581   .1434271     3.87   0.000     .2737462      .83597
   f_tu_elec |   .1286637   .2839381     0.45   0.650    -.4278448    .6851721
     tu_coup |  -.6321242   .3822147    -1.65   0.098    -1.381251    .1170029
    gas_disp |  -.1475907   .3159471    -0.47   0.640    -.7668357    .4716542
             |
  ln_tumilex |
        L12. |   1.283848   1.138431     1.13   0.259    -.9474367    3.515132
             |
  ln_grmilex |
        L12. |  -1.121425   2.531643    -0.44   0.658    -6.083355    3.840505
             |
  ln_tugdppc |
        L12. |  -.1460061   2.189863    -0.07   0.947    -4.438058    4.146046
             |
       _cons |   4.860011   22.44784     0.22   0.829    -39.13694    48.85697
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .2425618   .1585778     1.53   0.126     -.068245    .5533685
-------------+----------------------------------------------------------------
      /sigma |   .2917515   .0247211    11.80   0.000      .243299     .340204
------------------------------------------------------------------------------
Note: The test of the variance against zero is one sided, and the two-sided
      confidence interval is truncated at zero.

. arima ln_airvio l.approval_ip deadlyearthquake admin_fe4 admin_fe5 may f_tu_elec tu_coup gas_disp l12.ln_tumilex l12.ln_grmilex l12.ln_tugdppc if 
> modate>tm(2013m8), ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood = -15.785909  
Iteration 1:   log likelihood = -15.551749  
Iteration 2:   log likelihood = -15.447043  
Iteration 3:   log likelihood =  -15.27131  
Iteration 4:   log likelihood =   -15.2301  
(switching optimization to BFGS)
Iteration 5:   log likelihood = -15.223802  
Iteration 6:   log likelihood = -15.216045  
Iteration 7:   log likelihood = -15.214716  
Iteration 8:   log likelihood = -15.213225  
Iteration 9:   log likelihood =  -15.21267  
Iteration 10:  log likelihood = -15.212529  
Iteration 11:  log likelihood = -15.212407  
Iteration 12:  log likelihood = -15.212379  
Iteration 13:  log likelihood = -15.212369  
Iteration 14:  log likelihood = -15.212369  

ARIMA regression

Sample:  2013m10 - 2020m6                       Number of obs     =         81
                                                Wald chi2(12)     =     190.30
Log likelihood = -15.21237                      Prob > chi2       =     0.0000

----------------------------------------------------------------------------------
                 |                 OPG
       ln_airvio |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
ln_airvio        |
     approval_ip |
             L1. |  -.0318374   .0073743    -4.32   0.000    -.0462908   -.0173839
                 |
deadlyearthquake |  -.0120654   .2145337    -0.06   0.955    -.4325436    .4084129
       admin_fe4 |  -.0001353   .1729095    -0.00   0.999    -.3390316    .3387611
       admin_fe5 |   .2932874   .3240701     0.91   0.365    -.3418783    .9284532
             may |    .558533   .1407436     3.97   0.000     .2826806    .8343854
       f_tu_elec |   .1274973   .2871454     0.44   0.657    -.4352972    .6902919
         tu_coup |  -.6385309    .391327    -1.63   0.103    -1.405518    .1284559
        gas_disp |  -.1534034   .3131015    -0.49   0.624     -.767071    .4602643
                 |
      ln_tumilex |
            L12. |   1.286341   1.122172     1.15   0.252     -.913077    3.485758
                 |
      ln_grmilex |
            L12. |  -1.070989   2.479239    -0.43   0.666    -5.930208    3.788229
                 |
      ln_tugdppc |
            L12. |   -.127157   2.151492    -0.06   0.953    -4.344005    4.089691
                 |
           _cons |   4.233411   21.45149     0.20   0.844    -37.81074    46.27756
-----------------+----------------------------------------------------------------
ARMA             |
              ar |
             L1. |   .2375815   .1571953     1.51   0.131    -.0705156    .5456785
-----------------+----------------------------------------------------------------
          /sigma |   .2918593   .0247535    11.79   0.000     .2433433    .3403753
----------------------------------------------------------------------------------
Note: The test of the variance against zero is one sided, and the two-sided
      confidence interval is truncated at zero.

. arima ln_airvio l.approval_ip visit admin_fe4 admin_fe5 may f_tu_elec tu_coup gas_disp l12.ln_tumilex l12.ln_grmilex l12.ln_tugdppc if modate>tm(2
> 013m8), ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood = -14.927397  
Iteration 1:   log likelihood = -14.713429  
Iteration 2:   log likelihood =  -14.57404  
Iteration 3:   log likelihood = -14.434271  
Iteration 4:   log likelihood = -14.390589  
(switching optimization to BFGS)
Iteration 5:   log likelihood = -14.379462  
Iteration 6:   log likelihood = -14.377598  
Iteration 7:   log likelihood = -14.376225  
Iteration 8:   log likelihood = -14.375953  
Iteration 9:   log likelihood = -14.374203  
Iteration 10:  log likelihood =  -14.37365  
Iteration 11:  log likelihood = -14.373192  
Iteration 12:  log likelihood = -14.373079  
Iteration 13:  log likelihood = -14.373005  
Iteration 14:  log likelihood = -14.373003  
(switching optimization to BHHH)
Iteration 15:  log likelihood = -14.373001  
Iteration 16:  log likelihood =    -14.373  

ARIMA regression

Sample:  2013m10 - 2020m6                       Number of obs     =         81
                                                Wald chi2(12)     =     196.26
Log likelihood =   -14.373                      Prob > chi2       =     0.0000

------------------------------------------------------------------------------
             |                 OPG
   ln_airvio |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ln_airvio    |
 approval_ip |
         L1. |   -.032522   .0072691    -4.47   0.000    -.0467692   -.0182748
             |
       visit |  -.2644795   .4839076    -0.55   0.585    -1.212921     .683962
   admin_fe4 |   .0097624    .167722     0.06   0.954    -.3189667    .3384914
   admin_fe5 |   .2997655    .315933     0.95   0.343    -.3194519    .9189829
         may |   .5556811   .1388853     4.00   0.000     .2834708    .8278914
   f_tu_elec |   .1287618   .2736784     0.47   0.638     -.407638    .6651617
     tu_coup |  -.6497669    .392097    -1.66   0.097    -1.418263    .1187292
    gas_disp |  -.1545433   .3087761    -0.50   0.617    -.7597333    .4506466
             |
  ln_tumilex |
        L12. |   1.259775   1.086245     1.16   0.246    -.8692267    3.388776
             |
  ln_grmilex |
        L12. |  -.7771747   2.388111    -0.33   0.745    -5.457787    3.903438
             |
  ln_tugdppc |
        L12. |  -.2381515   2.083994    -0.11   0.909    -4.322704    3.846401
             |
       _cons |   3.096079   20.68492     0.15   0.881    -37.44562    43.63778
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .2340192   .1573401     1.49   0.137    -.0743618    .5424002
-------------+----------------------------------------------------------------
      /sigma |   .2888515   .0237926    12.14   0.000     .2422188    .3354842
------------------------------------------------------------------------------
Note: The test of the variance against zero is one sided, and the two-sided
      confidence interval is truncated at zero.

. arima ln_airvio l.approval_ip davut_exit admin_fe4 admin_fe5 may f_tu_elec tu_coup gas_disp l12.ln_tumilex l12.ln_grmilex l12.ln_tugdppc if modate
> >tm(2013m8), ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood = -15.565991  
Iteration 1:   log likelihood = -15.268537  
Iteration 2:   log likelihood = -15.150517  
Iteration 3:   log likelihood =  -14.95841  
Iteration 4:   log likelihood = -14.938773  
(switching optimization to BFGS)
Iteration 5:   log likelihood = -14.893299  
Iteration 6:   log likelihood = -14.881837  
Iteration 7:   log likelihood = -14.877504  
Iteration 8:   log likelihood = -14.876549  
Iteration 9:   log likelihood =  -14.87606  
Iteration 10:  log likelihood = -14.876015  
Iteration 11:  log likelihood = -14.875972  
Iteration 12:  log likelihood = -14.875966  
Iteration 13:  log likelihood = -14.875959  
Iteration 14:  log likelihood = -14.875958  
(switching optimization to BHHH)
Iteration 15:  log likelihood = -14.875957  

ARIMA regression

Sample:  2013m10 - 2020m6                       Number of obs     =         81
                                                Wald chi2(12)     =     191.76
Log likelihood = -14.87596                      Prob > chi2       =     0.0000

------------------------------------------------------------------------------
             |                 OPG
   ln_airvio |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ln_airvio    |
 approval_ip |
         L1. |  -.0293203   .0092493    -3.17   0.002    -.0474486    -.011192
             |
  davut_exit |  -.1733152   .2954669    -0.59   0.557    -.7524196    .4057892
   admin_fe4 |   .0054086   .1716987     0.03   0.975    -.3311147     .341932
   admin_fe5 |   .2817608   .3220654     0.87   0.382    -.3494758    .9129973
         may |   .5641978   .1417204     3.98   0.000     .2864308    .8419647
   f_tu_elec |   .1246265   .3058904     0.41   0.684    -.4749076    .7241606
     tu_coup |  -.5731869    .378144    -1.52   0.130    -1.314335    .1679616
    gas_disp |  -.1745061     .30799    -0.57   0.571    -.7781554    .4291432
             |
  ln_tumilex |
        L12. |    1.12231   1.211517     0.93   0.354     -1.25222     3.49684
             |
  ln_grmilex |
        L12. |  -.1355877   3.041108    -0.04   0.964     -6.09605    5.824875
             |
  ln_tugdppc |
        L12. |    .844192   2.821226     0.30   0.765     -4.68531    6.373693
             |
       _cons |  -11.82086   36.28671    -0.33   0.745     -82.9415    59.29978
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .2564531   .1733293     1.48   0.139    -.0832661    .5961724
-------------+----------------------------------------------------------------
      /sigma |   .2906303   .0247949    11.72   0.000     .2420332    .3392273
------------------------------------------------------------------------------
Note: The test of the variance against zero is one sided, and the two-sided
      confidence interval is truncated at zero.

. 
. arima ln_airvio l.approval_ip natosummit admin_fe4 admin_fe5 may f_tu_elec tu_coup gas_disp l12.ln_tumilex l12.ln_grmilex l12.ln_tugdppc if modate
> >tm(2013m8), ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood = -14.469966  
Iteration 1:   log likelihood = -14.293627  
Iteration 2:   log likelihood = -14.168472  
Iteration 3:   log likelihood = -14.130558  
Iteration 4:   log likelihood = -14.082264  
(switching optimization to BFGS)
Iteration 5:   log likelihood = -14.063363  
Iteration 6:   log likelihood = -14.041118  
Iteration 7:   log likelihood = -14.034615  
Iteration 8:   log likelihood = -14.028853  
Iteration 9:   log likelihood = -14.025941  
Iteration 10:  log likelihood = -14.024699  
Iteration 11:  log likelihood = -14.024032  
Iteration 12:  log likelihood = -14.023814  
Iteration 13:  log likelihood =  -14.02379  
Iteration 14:  log likelihood = -14.023783  
(switching optimization to BHHH)
Iteration 15:  log likelihood = -14.023782  

ARIMA regression

Sample:  2013m10 - 2020m6                       Number of obs     =         81
                                                Wald chi2(12)     =     197.43
Log likelihood = -14.02378                      Prob > chi2       =     0.0000

------------------------------------------------------------------------------
             |                 OPG
   ln_airvio |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ln_airvio    |
 approval_ip |
         L1. |  -.0339588   .0070729    -4.80   0.000    -.0478216   -.0200961
             |
  natosummit |   .2389295   .2153438     1.11   0.267    -.1831367    .6609956
   admin_fe4 |  -.0520153   .1861791    -0.28   0.780    -.4169197     .312889
   admin_fe5 |   .1696874   .3377863     0.50   0.615    -.4923617    .8317364
         may |   .5280754   .1546368     3.41   0.001     .2249928     .831158
   f_tu_elec |   .1436795   .2631248     0.55   0.585    -.3720357    .6593947
     tu_coup |  -.9219984   .5123897    -1.80   0.072    -1.926264     .082267
    gas_disp |   -.096994   .2925618    -0.33   0.740    -.6704046    .4764166
             |
  ln_tumilex |
        L12. |   1.192914   1.064965     1.12   0.263    -.8943786    3.280207
             |
  ln_grmilex |
        L12. |  -.9154313   2.429125    -0.38   0.706     -5.67643    3.845567
             |
  ln_tugdppc |
        L12. |   .2793494    2.21927     0.13   0.900     -4.07034    4.629038
             |
       _cons |  -.0847395   22.43509    -0.00   0.997    -44.05671    43.88723
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .2083124   .1554113     1.34   0.180    -.0962881    .5129129
-------------+----------------------------------------------------------------
      /sigma |   .2876303   .0240935    11.94   0.000     .2404079    .3348528
------------------------------------------------------------------------------
Note: The test of the variance against zero is one sided, and the two-sided
      confidence interval is truncated at zero.

. arima ln_airvio l.approval_ip syria_op admin_fe4 admin_fe5 may f_tu_elec tu_coup gas_disp l12.ln_tumilex l12.ln_grmilex l12.ln_tugdppc if modate>t
> m(2013m8), ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood = -13.896539  
Iteration 1:   log likelihood = -13.552275  
Iteration 2:   log likelihood = -13.516671  
Iteration 3:   log likelihood = -13.467793  
Iteration 4:   log likelihood = -13.336052  
(switching optimization to BFGS)
Iteration 5:   log likelihood = -13.304206  
Iteration 6:   log likelihood = -13.266521  
Iteration 7:   log likelihood = -13.252298  
Iteration 8:   log likelihood = -13.249529  
Iteration 9:   log likelihood =  -13.24698  
Iteration 10:  log likelihood = -13.245692  
Iteration 11:  log likelihood = -13.245611  
Iteration 12:  log likelihood = -13.245557  
Iteration 13:  log likelihood = -13.245545  
Iteration 14:  log likelihood = -13.245536  
(switching optimization to BHHH)
Iteration 15:  log likelihood = -13.245533  
Iteration 16:  log likelihood = -13.245533  

ARIMA regression

Sample:  2013m10 - 2020m6                       Number of obs     =         81
                                                Wald chi2(12)     =     202.83
Log likelihood = -13.24553                      Prob > chi2       =     0.0000

------------------------------------------------------------------------------
             |                 OPG
   ln_airvio |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ln_airvio    |
 approval_ip |
         L1. |  -.0268126   .0095193    -2.82   0.005    -.0454701   -.0081551
             |
    syria_op |  -.2421049   .1543313    -1.57   0.117    -.5445887    .0603788
   admin_fe4 |  -.0077023   .1708504    -0.05   0.964    -.3425629    .3271583
   admin_fe5 |   .1935951   .3359355     0.58   0.564    -.4648265    .8520166
         may |   .5162664   .1405417     3.67   0.000     .2408098    .7917231
   f_tu_elec |    .160057   .3548924     0.45   0.652    -.5355193    .8556332
     tu_coup |  -.7031146   .3862192    -1.82   0.069     -1.46009    .0538611
    gas_disp |  -.0977927   .3354067    -0.29   0.771    -.7551776    .5595923
             |
  ln_tumilex |
        L12. |   .9817117   1.239484     0.79   0.428    -1.447632    3.411056
             |
  ln_grmilex |
        L12. |  -.6834248   2.400127    -0.28   0.776    -5.387587    4.020738
             |
  ln_tugdppc |
        L12. |   .8902412   2.290705     0.39   0.698    -3.599457     5.37994
             |
       _cons |    -6.3996   21.78405    -0.29   0.769    -49.09555    36.29635
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .2504398   .1683748     1.49   0.137    -.0795688    .5804483
-------------+----------------------------------------------------------------
      /sigma |   .2848417   .0238183    11.96   0.000     .2381586    .3315248
------------------------------------------------------------------------------
Note: The test of the variance against zero is one sided, and the two-sided
      confidence interval is truncated at zero.

. arima ln_airvio l.approval_ip l.event admin_fe5 may f_tu_elec tu_coup gas_disp l12.ln_tumilex l12.ln_grmilex l12.ln_tugdppc if modate>tm(2013m8), 
> ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood = -13.276666  
Iteration 1:   log likelihood = -12.906051  
Iteration 2:   log likelihood = -12.903985  
Iteration 3:   log likelihood =  -12.79402  
Iteration 4:   log likelihood = -12.719044  
(switching optimization to BFGS)
Iteration 5:   log likelihood = -12.656293  
Iteration 6:   log likelihood = -12.655669  
Iteration 7:   log likelihood = -12.634274  
Iteration 8:   log likelihood = -12.625331  
Iteration 9:   log likelihood = -12.625063  
Iteration 10:  log likelihood = -12.623713  
Iteration 11:  log likelihood = -12.623652  
Iteration 12:  log likelihood = -12.623524  
Iteration 13:  log likelihood = -12.623485  
BFGS stepping has contracted, resetting BFGS Hessian (0)
Iteration 14:  log likelihood = -12.623462  
(switching optimization to BHHH)
Iteration 15:  log likelihood = -12.623462  (backed up)
Iteration 16:  log likelihood = -12.623461  
Iteration 17:  log likelihood = -12.623458  
Iteration 18:  log likelihood = -12.623457  
Iteration 19:  log likelihood = -12.623457  
(switching optimization to BFGS)
Iteration 20:  log likelihood = -12.623456  
Iteration 21:  log likelihood = -12.623456  
BFGS stepping has contracted, resetting BFGS Hessian (1)
Iteration 22:  log likelihood = -12.623455  
Iteration 23:  log likelihood = -12.623455  (backed up)
Iteration 24:  log likelihood = -12.623455  (backed up)
Iteration 25:  log likelihood = -12.623455  

ARIMA regression

Sample:  2016m2 - 2020m6                        Number of obs     =         53
                                                Wald chi2(11)     =     128.33
Log likelihood = -12.62346                      Prob > chi2       =     0.0000

------------------------------------------------------------------------------
             |                 OPG
   ln_airvio |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ln_airvio    |
 approval_ip |
         L1. |  -.0360902   .0105535    -3.42   0.001    -.0567746   -.0154058
             |
       event |
         L1. |  -.0010538   .0024419    -0.43   0.666    -.0058398    .0037322
             |
   admin_fe5 |   .2659913   .3383994     0.79   0.432    -.3972594     .929242
         may |   .5526782   .1634355     3.38   0.001     .2323505     .873006
   f_tu_elec |   .0253592    .397159     0.06   0.949    -.7530582    .8037766
     tu_coup |  -.5341806   .4253834    -1.26   0.209    -1.367917    .2995555
    gas_disp |  -.1230775   .4114763    -0.30   0.765    -.9295562    .6834012
             |
  ln_tumilex |
        L12. |  -.3899179   3.304741    -0.12   0.906    -6.867092    6.087256
             |
  ln_grmilex |
        L12. |   3.961884   11.59116     0.34   0.732    -18.75638    26.68015
             |
  ln_tugdppc |
        L12. |   2.427875   6.503183     0.37   0.709    -10.31813    15.17388
             |
       _cons |  -47.93134    114.081    -0.42   0.674    -271.5261    175.6634
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .3060266   .2224472     1.38   0.169     -.129962    .7420152
-------------+----------------------------------------------------------------
      /sigma |   .3067608    .037089     8.27   0.000     .2340677    .3794539
------------------------------------------------------------------------------
Note: The test of the variance against zero is one sided, and the two-sided
      confidence interval is truncated at zero.

. arima ln_airvio l.approval_ip l.event_kurds admin_fe5 may f_tu_elec tu_coup gas_disp l12.ln_tumilex l12.ln_grmilex l12.ln_tugdppc if modate>tm(201
> 3m8), ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood = -13.610204  
Iteration 1:   log likelihood = -13.234673  
Iteration 2:   log likelihood = -12.860783  
Iteration 3:   log likelihood = -12.807183  
Iteration 4:   log likelihood = -12.792943  
(switching optimization to BFGS)
Iteration 5:   log likelihood = -12.785197  
Iteration 6:   log likelihood =  -12.78103  
Iteration 7:   log likelihood = -12.778711  
Iteration 8:   log likelihood = -12.776943  
Iteration 9:   log likelihood = -12.776865  
Iteration 10:  log likelihood = -12.776563  
Iteration 11:  log likelihood = -12.776516  
Iteration 12:  log likelihood = -12.776495  
Iteration 13:  log likelihood =  -12.77649  
Iteration 14:  log likelihood = -12.776488  
(switching optimization to BHHH)
Iteration 15:  log likelihood = -12.776488  

ARIMA regression

Sample:  2016m2 - 2020m6                        Number of obs     =         53
                                                Wald chi2(11)     =     126.18
Log likelihood = -12.77649                      Prob > chi2       =     0.0000

------------------------------------------------------------------------------
             |                 OPG
   ln_airvio |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ln_airvio    |
 approval_ip |
         L1. |  -.0365876   .0110088    -3.32   0.001    -.0581646   -.0150107
             |
 event_kurds |
         L1. |    .000101   .0029699     0.03   0.973    -.0057198    .0059218
             |
   admin_fe5 |    .198304   .3464842     0.57   0.567    -.4807926    .8774007
         may |   .5352594   .1600968     3.34   0.001     .2214755    .8490433
   f_tu_elec |    .049669   .4625614     0.11   0.914    -.8569347    .9562727
     tu_coup |  -.5414057    .392344    -1.38   0.168    -1.310386    .2275745
    gas_disp |   -.138247   .4096215    -0.34   0.736    -.9410904    .6645965
             |
  ln_tumilex |
        L12. |  -.1072431   3.467444    -0.03   0.975    -6.903309    6.688822
             |
  ln_grmilex |
        L12. |   3.655692   12.24961     0.30   0.765     -20.3531    27.66449
             |
  ln_tugdppc |
        L12. |   3.127815   7.059272     0.44   0.658     -10.7081    16.96373
             |
       _cons |  -55.01025   119.5844    -0.46   0.646    -289.3913    179.3708
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .3385793   .2347952     1.44   0.149    -.1216107    .7987693
-------------+----------------------------------------------------------------
      /sigma |   .3075772   .0369274     8.33   0.000     .2352008    .3799535
------------------------------------------------------------------------------
Note: The test of the variance against zero is one sided, and the two-sided
      confidence interval is truncated at zero.

. arima ln_airvio l.approval_ip l.ln_trade admin_fe4 admin_fe5 may f_tu_elec tu_coup gas_disp l12.ln_tumilex l12.ln_grmilex l12.ln_tugdppc if modate
> >tm(2013m8), ar(1)

(setting optimization to BHHH)
Iteration 0:   log likelihood = -12.648156  
Iteration 1:   log likelihood = -12.513544  
Iteration 2:   log likelihood = -12.444894  
Iteration 3:   log likelihood = -12.399018  
Iteration 4:   log likelihood = -12.374904  
(switching optimization to BFGS)
Iteration 5:   log likelihood = -12.368044  
Iteration 6:   log likelihood =  -12.36425  
Iteration 7:   log likelihood = -12.357684  
Iteration 8:   log likelihood = -12.353064  
Iteration 9:   log likelihood = -12.350023  
Iteration 10:  log likelihood = -12.347858  
Iteration 11:  log likelihood = -12.347451  
Iteration 12:  log likelihood = -12.347329  
Iteration 13:  log likelihood = -12.347317  
Iteration 14:  log likelihood = -12.347315  
(switching optimization to BHHH)
Iteration 15:  log likelihood = -12.347315  

ARIMA regression

Sample:  2013m10 - 2020m5                       Number of obs     =         80
                                                Wald chi2(12)     =     186.62
Log likelihood = -12.34732                      Prob > chi2       =     0.0000

------------------------------------------------------------------------------
             |                 OPG
   ln_airvio |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ln_airvio    |
 approval_ip |
         L1. |  -.0305761   .0075707    -4.04   0.000    -.0454144   -.0157378
             |
 ln_tradevol |
         L1. |   .4006035    .210512     1.90   0.057    -.0119925    .8131994
             |
   admin_fe4 |   .1512201   .1830087     0.83   0.409    -.2074703    .5099105
   admin_fe5 |   .3734783   .3379103     1.11   0.269    -.2888138     1.03577
         may |   .6124533   .1516547     4.04   0.000     .3152155     .909691
   f_tu_elec |    .138105   .2971184     0.46   0.642    -.4442363    .7204463
     tu_coup |   -.716392     .54891    -1.31   0.192    -1.792236    .3594517
    gas_disp |   .0102211    .348687     0.03   0.977    -.6731929    .6936352
             |
  ln_tumilex |
        L12. |   .6021968    1.16691     0.52   0.606    -1.684904    2.889298
             |
  ln_grmilex |
        L12. |  -.1632105   2.172933    -0.08   0.940     -4.42208    4.095659
             |
  ln_tugdppc |
        L12. |   .8440324   2.416052     0.35   0.727    -3.891342    5.579406
             |
       _cons |  -14.64264   23.91281    -0.61   0.540    -61.51088     32.2256
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .1791744   .1632052     1.10   0.272    -.1407019    .4990506
-------------+----------------------------------------------------------------
      /sigma |   .2822949    .025577    11.04   0.000      .232165    .3324249
------------------------------------------------------------------------------
Note: The test of the variance against zero is one sided, and the two-sided
      confidence interval is truncated at zero.

. 
. * Table A12 - AR Poisson
. * If arpois package is not installed: net install sts13.pkg
. 
. gen lag_approval=l.approval_ip
(127 missing values generated)

. gen lag_econperc=l.econ_improve_ip
(157 missing values generated)

. gen lag12_tumil=l.l12.ln_tumilex
(13 missing values generated)

. gen lag12_gremil=l.l12.ln_grmilex
(13 missing values generated)

. gen lag12_gdp=l.l12.ln_tugdppc
(13 missing values generated)

. 
. arpois airvio lag_approval admin_fe4 admin_fe5 may if modate>tm(2013m8), ar(1) delete
log-linear autoregressive model 1 order

(sum of wgt is 10171.252)

Iteration 0:   residual SS =  6.768815
Iteration 1:   residual SS =  6.312141

      Source |       SS       df       MS            Number of obs =        80
-------------+------------------------------         F(  5,    74) =     22.61
       Model |   9.6446945     5   1.9289389         Prob > F      =    0.0000
    Residual |  6.31214072    74  .085299199         R-squared     =    0.6044
-------------+------------------------------         Adj R-squared =    0.5777
       Total |  15.9568352    79  .201985256         Root MSE      =  .2920603
                                                     Res. dev.     =   28.0064
(aphea)
------------------------------------------------------------------------------
          _z |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          X0 |   5.352243   .3654966    14.64   0.000     4.623975    6.080511
          X1 |  -.0181247   .0076309    -2.38   0.020    -.0333297   -.0029198
          X2 |   .0942916   .1157426     0.81   0.418    -.1363305    .3249137
          X3 |   .5737694   .1172099     4.90   0.000     .3402235    .8073152
          X4 |   .5600312   .0962772     5.82   0.000     .3681947    .7518677
          R1 |   .2714613   .1173595     2.31   0.024     .0376173    .5053053
------------------------------------------------------------------------------
* Parameter X0 taken as constant term in model & ANOVA table
 (SEs, P values, CIs, and correlations are asymptotic approximations)

. arpois airvio lag_approval admin_fe4 admin_fe5 may f_tu_elec tu_coup gas_disp if modate>tm(2013m8), ar(1) delete
log-linear autoregressive model 1 order

(sum of wgt is 10171.673)

Iteration 0:   residual SS =  6.164282
Iteration 1:   residual SS =  5.949095

      Source |       SS       df       MS            Number of obs =        80
-------------+------------------------------         F(  8,    71) =     14.51
       Model |  9.72449249     8  1.21556156         Prob > F      =    0.0000
    Residual |  5.94909535    71  .083790075         R-squared     =    0.6204
-------------+------------------------------         Adj R-squared =    0.5777
       Total |  15.6735878    79  .198399846         Root MSE      =  .2894652
                                                     Res. dev.     =  23.84889
(aphea)
------------------------------------------------------------------------------
          _z |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          X0 |   5.321466     .36147    14.72   0.000     4.600715    6.042216
          X1 |  -.0176687   .0075025    -2.36   0.021    -.0326283   -.0027091
          X2 |   .0955031     .11475     0.83   0.408     -.133302    .3243082
          X3 |   .5503587   .1677256     3.28   0.002     .2159233     .884794
          X4 |   .5122204   .1026124     4.99   0.000     .3076171    .7168238
          X5 |   .2111833   .1337513     1.58   0.119    -.0555092    .4778758
          X6 |  -1.068954   .5909136    -1.81   0.075    -2.247202    .1092948
          X7 |   .0533411   .1420397     0.38   0.708    -.2298781    .3365602
          R1 |    .191658   .1199517     1.60   0.115    -.0475189    .4308349
------------------------------------------------------------------------------
* Parameter X0 taken as constant term in model & ANOVA table
 (SEs, P values, CIs, and correlations are asymptotic approximations)

. arpois airvio lag_approval admin_fe5 may f_tu_elec tu_coup gas_disp lag_econperc if modate>tm(2013m8), ar(1) delete
log-linear autoregressive model 1 order

(sum of wgt is 8562.7528)

Iteration 0:   residual SS =  5.089915
Iteration 1:   residual SS =  4.764309

      Source |       SS       df       MS            Number of obs =        64
-------------+------------------------------         F(  8,    55) =     11.65
       Model |  8.07553797     8  1.00944225         Prob > F      =    0.0000
    Residual |  4.76430928    55  .086623805         R-squared     =    0.6289
-------------+------------------------------         Adj R-squared =    0.5750
       Total |  12.8398473    63  .203807099         Root MSE      =  .2943192
                                                     Res. dev.     =  19.60544
(aphea)
------------------------------------------------------------------------------
          _z |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          X0 |    5.12049   .4615606    11.09   0.000     4.195501    6.045478
          X1 |  -.0235308   .0085311    -2.76   0.008    -.0406275   -.0064342
          X2 |   .5529992   .1617445     3.42   0.001     .2288559    .8771425
          X3 |    .503207   .1169464     4.30   0.000     .2688411    .7375729
          X4 |   .2421333   .1578089     1.53   0.131    -.0741228    .5583894
          X5 |  -1.078767   .6177098    -1.75   0.086    -2.316685    .1591515
          X6 |   .0890237   .1503881     0.59   0.556    -.2123608    .3904082
          X7 |   .0154082   .0099527     1.55   0.127    -.0045374    .0353539
          R1 |   .2565386   .1327106     1.93   0.058    -.0094194    .5224966
------------------------------------------------------------------------------
* Parameter X0 taken as constant term in model & ANOVA table
 (SEs, P values, CIs, and correlations are asymptotic approximations)

. arpois airvio lag_approval admin_fe4 admin_fe5 may f_tu_elec tu_coup gas_disp lag12_tumil lag12_gremil lag12_gdp if modate>tm(2013m8), ar(1) delet
> e
log-linear autoregressive model 1 order

(sum of wgt is 10167.889)

Iteration 0:   residual SS =  5.952384
Iteration 1:   residual SS =  5.801083
Iteration 2:   residual SS =  5.801083

      Source |       SS       df       MS            Number of obs =        80
-------------+------------------------------         F( 11,    68) =     10.50
       Model |  9.84972553    11  .895429594         Prob > F      =    0.0000
    Residual |  5.80108293    68  .085310043         R-squared     =    0.6293
-------------+------------------------------         Adj R-squared =    0.5694
       Total |  15.6508085    79  .198111499         Root MSE      =  .2920788
                                                     Res. dev.     =  21.95576
(aphea)
------------------------------------------------------------------------------
          _z |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          X0 |  -14.01352   15.16775    -0.92   0.359    -44.28029    16.25325
          X1 |  -.0249559   .0090926    -2.74   0.008    -.0430998    -.006812
          X2 |   .0371235   .1613953     0.23   0.819    -.2849359    .3591828
          X3 |   .4320016   .2654689     1.63   0.108    -.0977334    .9617365
          X4 |   .5207727   .1044483     4.99   0.000     .3123493    .7291961
          X5 |   .2213574   .1378357     1.61   0.113    -.0536894    .4964042
          X6 |  -1.108727   .5987134    -1.85   0.068    -2.303441    .0859868
          X7 |  -.0166451   .1804662    -0.09   0.927    -.3767597    .3434695
          X8 |  -.3489235   .7313065    -0.48   0.635    -1.808223    1.110376
          X9 |   2.308536   1.968838     1.17   0.245    -1.620219    6.237291
         X10 |   .3345876   1.595095     0.21   0.834    -2.848374     3.51755
          R1 |   .1588556   .1236702     1.28   0.203    -.0879245    .4056357
------------------------------------------------------------------------------
* Parameter X0 taken as constant term in model & ANOVA table
 (SEs, P values, CIs, and correlations are asymptotic approximations)

. 
. * Table A13 - Lag Order Four + reversed contemporaneous effects
. svar ln_airvio approval_ip if modate>tm(2013m8), exog(admin_fe4 admin_fe5 may f_tu_elec tu_coup gas_disp l12.ln_tumilex l12.ln_grmilex l12.ln_tugd
> ppc) ///
> lags(1 2 3 4) aeq(A1) beq(B1) var

Vector autoregression

Sample:  2014m1 - 2020m5                        Number of obs     =         77
Log likelihood =  -191.9257                     AIC               =   5.920149
FPE            =   1.299538                     HQIC              =   6.358462
Det(Sigma_ml)  =   .5012402                     SBIC              =   7.015954

Equation           Parms      RMSE     R-sq      chi2     P>chi2
----------------------------------------------------------------
ln_airvio            18     .301304   0.7015   180.9591   0.0000
approval_ip          18     3.17882   0.6599   149.4162   0.0000
----------------------------------------------------------------

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ln_airvio    |
   ln_airvio |
         L1. |   .1700085    .103726     1.64   0.101    -.0332908    .3733078
         L2. |  -.0219253   .1015897    -0.22   0.829    -.2210375    .1771869
         L3. |    -.25602    .105053    -2.44   0.015    -.4619201     -.05012
         L4. |   .0532574   .0998262     0.53   0.594    -.1423984    .2489133
             |
 approval_ip |
         L1. |  -.0329629   .0089382    -3.69   0.000    -.0504814   -.0154445
         L2. |  -.0082126   .0093613    -0.88   0.380    -.0265604    .0101351
         L3. |   .0026548   .0103783     0.26   0.798    -.0176862    .0229958
         L4. |   .0176707   .0095067     1.86   0.063     -.000962    .0363034
             |
   admin_fe4 |    .039524   .1305538     0.30   0.762    -.2163567    .2954046
   admin_fe5 |   .4820252   .2431273     1.98   0.047     .0055045    .9585459
         may |   .5750788   .1181612     4.87   0.000     .3434872    .8066704
   f_tu_elec |    .182896   .1338045     1.37   0.172     -.079356    .4451481
     tu_coup |  -.7473338   .2920141    -2.56   0.010    -1.319671   -.1749967
    gas_disp |  -.2290783   .2222373    -1.03   0.303    -.6646555    .2064989
             |
  ln_tumilex |
        L12. |   1.528958   .8853379     1.73   0.084    -.2062728    3.264188
             |
  ln_grmilex |
        L12. |   -.466648   2.859788    -0.16   0.870     -6.07173    5.138433
             |
  ln_tugdppc |
        L12. |  -1.886565   2.002458    -0.94   0.346     -5.81131     2.03818
             |
       _cons |   13.82066   15.44004     0.90   0.371    -16.44126    44.08257
-------------+----------------------------------------------------------------
approval_ip  |
   ln_airvio |
         L1. |  -1.355136   1.094333    -1.24   0.216     -3.49999    .7897184
         L2. |  -1.113189   1.071795    -1.04   0.299    -3.213868    .9874896
         L3. |  -.4861551   1.108333    -0.44   0.661    -2.658447    1.686137
         L4. |  -2.613705    1.05319    -2.48   0.013    -4.677919   -.5494915
             |
 approval_ip |
         L1. |   .3259319   .0942997     3.46   0.001     .1411079    .5107558
         L2. |   .0183631   .0987633     0.19   0.852    -.1752095    .2119357
         L3. |  -.0580828   .1094929    -0.53   0.596     -.272685    .1565194
         L4. |  -.0547786   .1002975    -0.55   0.585     -.251358    .1418008
             |
   admin_fe4 |  -1.289486   1.377372    -0.94   0.349    -3.989086    1.410113
   admin_fe5 |  -1.896047   2.565048    -0.74   0.460    -6.923448    3.131355
         may |  -.7713135   1.246627    -0.62   0.536    -3.214658    1.672031
   f_tu_elec |   -1.13167   1.411668    -0.80   0.423    -3.898488    1.635149
     tu_coup |   21.98148   3.080815     7.13   0.000     15.94319    28.01977
    gas_disp |  -2.332059   2.344655    -0.99   0.320    -6.927497     2.26338
             |
  ln_tumilex |
        L12. |    13.3952   9.340517     1.43   0.152     -4.91188    31.70227
             |
  ln_grmilex |
        L12. |   25.20856   30.17142     0.84   0.403    -33.92633    84.34345
             |
  ln_tugdppc |
        L12. |  -15.53917   21.12639    -0.74   0.462    -56.94612    25.86779
             |
       _cons |  -127.3089   162.8959    -0.78   0.434    -446.5791    191.9612
------------------------------------------------------------------------------
Estimating short-run parameters

Iteration 0:   log likelihood = -398.57108  
Iteration 1:   log likelihood = -220.86471  
Iteration 2:   log likelihood = -192.59324  
Iteration 3:   log likelihood = -191.92911  
Iteration 4:   log likelihood = -191.92574  
Iteration 5:   log likelihood = -191.92574  

Structural vector autoregression

 ( 1)  [a_1_1]_cons = 1
 ( 2)  [a_1_2]_cons = 0
 ( 3)  [a_2_2]_cons = 1
 ( 4)  [b_1_2]_cons = 0
 ( 5)  [b_2_1]_cons = 0

Sample:  2014m1 - 2020m5                        Number of obs     =         77
Exactly identified model                        Log likelihood    =  -191.9257

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      /a_1_1 |          1  (constrained)
      /a_2_1 |   2.778495   1.159866     2.40   0.017     .5051995    5.051791
      /a_1_2 |          0  (constrained)
      /a_2_2 |          1  (constrained)
-------------+----------------------------------------------------------------
      /b_1_1 |   .2637454   .0212532    12.41   0.000     .2220899    .3054009
      /b_2_1 |          0  (constrained)
      /b_1_2 |          0  (constrained)
      /b_2_2 |   2.684343   .2163106    12.41   0.000     2.260383    3.108304
------------------------------------------------------------------------------

. vargranger

   Granger causality Wald tests
  +------------------------------------------------------------------+
  |          Equation           Excluded |   chi2     df Prob > chi2 |
  |--------------------------------------+---------------------------|
  |         ln_airvio        approval_ip |  17.982     4    0.001    |
  |         ln_airvio                ALL |  17.982     4    0.001    |
  |--------------------------------------+---------------------------|
  |       approval_ip          ln_airvio |  8.4992     4    0.075    |
  |       approval_ip                ALL |  8.4992     4    0.075    |
  +------------------------------------------------------------------+

. svar  approval_ip ln_airvio if modate>tm(2013m8), exog(admin_fe4 admin_fe5 may f_tu_elec tu_coup gas_disp l12.ln_tumilex l12.ln_grmilex l12.ln_tug
> dppc) ///
> lags(1) aeq(A1) beq(B1) var

Vector autoregression

Sample:  2013m10 - 2020m5                       Number of obs     =         80
Log likelihood =  -211.8886                     AIC               =   5.897214
FPE            =   1.253603                     HQIC              =   6.183721
Det(Sigma_ml)  =   .6848604                     SBIC              =   6.611822

Equation           Parms      RMSE     R-sq      chi2     P>chi2
----------------------------------------------------------------
approval_ip          12     3.13383   0.6224   131.8606   0.0000
ln_airvio            12     .315985   0.6346   138.9227   0.0000
----------------------------------------------------------------

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
approval_ip  |
 approval_ip |
         L1. |   .3830308   .0923063     4.15   0.000     .2021137    .5639478
             |
   ln_airvio |
         L1. |  -.7248289   .9626499    -0.75   0.451    -2.611588     1.16193
             |
   admin_fe4 |  -1.611283   1.406986    -1.15   0.252    -4.368925     1.14636
   admin_fe5 |  -4.030119   2.459466    -1.64   0.101    -8.850583    .7903453
         may |  -.6472647   1.230798    -0.53   0.599    -3.059584    1.765054
   f_tu_elec |  -1.098664   1.444376    -0.76   0.447     -3.92959    1.732261
     tu_coup |   21.24106   3.006613     7.06   0.000      15.3482    27.13391
    gas_disp |  -.8549833   2.041968    -0.42   0.675    -4.857166      3.1472
             |
  ln_tumilex |
        L12. |   1.501343   7.951105     0.19   0.850    -14.08254    17.08522
             |
  ln_grmilex |
        L12. |   31.60938   17.83824     1.77   0.076    -3.352917    66.57168
             |
  ln_tugdppc |
        L12. |   2.228522   15.53246     0.14   0.886    -28.21454    32.67158
             |
       _cons |  -273.3925   149.4074    -1.83   0.067    -566.2256    19.44067
-------------+----------------------------------------------------------------
ln_airvio    |
 approval_ip |
         L1. |  -.0284231   .0093073    -3.05   0.002     -.046665   -.0101811
             |
   ln_airvio |
         L1. |   .1897679   .0970644     1.96   0.051    -.0004748    .3800106
             |
   admin_fe4 |  -.0028288    .141867    -0.02   0.984     -.280883    .2752255
   admin_fe5 |   .3025964   .2479889     1.22   0.222    -.1834529    .7886458
         may |   .5557465   .1241018     4.48   0.000     .3125114    .7989816
   f_tu_elec |   .1542638   .1456371     1.06   0.289    -.1311796    .4397072
     tu_coup |  -.9119225    .303158    -3.01   0.003    -1.506101   -.3177437
    gas_disp |  -.1697917   .2058924    -0.82   0.410    -.5733334      .23375
             |
  ln_tumilex |
        L12. |   .7573055   .8017132     0.94   0.345    -.8140235    2.328635
             |
  ln_grmilex |
        L12. |   .0229253   1.798637     0.01   0.990    -3.502338    3.548188
             |
  ln_tugdppc |
        L12. |  -.1033197   1.566144    -0.07   0.947    -3.172906    2.966267
             |
       _cons |  -1.365894   15.06481    -0.09   0.928    -30.89238    28.16059
------------------------------------------------------------------------------
Estimating short-run parameters

Iteration 0:   log likelihood = -749.95676  
Iteration 1:   log likelihood = -693.33306  
Iteration 2:   log likelihood = -478.09153  
Iteration 3:   log likelihood = -283.12712  
Iteration 4:   log likelihood = -246.51025  
Iteration 5:   log likelihood = -212.34963  
Iteration 6:   log likelihood = -211.89077  
Iteration 7:   log likelihood = -211.88855  
Iteration 8:   log likelihood = -211.88855  

Structural vector autoregression

 ( 1)  [a_1_1]_cons = 1
 ( 2)  [a_1_2]_cons = 0
 ( 3)  [a_2_2]_cons = 1
 ( 4)  [b_1_2]_cons = 0
 ( 5)  [b_2_1]_cons = 0

Sample:  2013m10 - 2020m5                       Number of obs     =         80
Exactly identified model                        Log likelihood    =  -211.8886

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      /a_1_1 |          1  (constrained)
      /a_2_1 |   .0184069   .0110837     1.66   0.097    -.0033169    .0401306
      /a_1_2 |          0  (constrained)
      /a_2_2 |          1  (constrained)
-------------+----------------------------------------------------------------
      /b_1_1 |   2.889247    .228415    12.65   0.000     2.441562    3.336932
      /b_2_1 |          0  (constrained)
      /b_1_2 |          0  (constrained)
      /b_2_2 |   .2864286   .0226442    12.65   0.000     .2420468    .3308103
------------------------------------------------------------------------------

. vargranger

   Granger causality Wald tests
  +------------------------------------------------------------------+
  |          Equation           Excluded |   chi2     df Prob > chi2 |
  |--------------------------------------+---------------------------|
  |       approval_ip          ln_airvio |  .56694     1    0.451    |
  |       approval_ip                ALL |  .56694     1    0.451    |
  |--------------------------------------+---------------------------|
  |         ln_airvio        approval_ip |   9.326     1    0.002    |
  |         ln_airvio                ALL |   9.326     1    0.002    |
  +------------------------------------------------------------------+

. 
. * Figure A2
. svar ln_airvio approval_ip if modate>tm(2013m8), exog(admin_fe4 admin_fe5 may f_tu_elec tu_coup gas_disp l12.ln_tumilex l12.ln_grmilex l12.ln_tugd
> ppc) ///
> lags(1 2 3 4) aeq(A1) beq(B1) var

Vector autoregression

Sample:  2014m1 - 2020m5                        Number of obs     =         77
Log likelihood =  -191.9257                     AIC               =   5.920149
FPE            =   1.299538                     HQIC              =   6.358462
Det(Sigma_ml)  =   .5012402                     SBIC              =   7.015954

Equation           Parms      RMSE     R-sq      chi2     P>chi2
----------------------------------------------------------------
ln_airvio            18     .301304   0.7015   180.9591   0.0000
approval_ip          18     3.17882   0.6599   149.4162   0.0000
----------------------------------------------------------------

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ln_airvio    |
   ln_airvio |
         L1. |   .1700085    .103726     1.64   0.101    -.0332908    .3733078
         L2. |  -.0219253   .1015897    -0.22   0.829    -.2210375    .1771869
         L3. |    -.25602    .105053    -2.44   0.015    -.4619201     -.05012
         L4. |   .0532574   .0998262     0.53   0.594    -.1423984    .2489133
             |
 approval_ip |
         L1. |  -.0329629   .0089382    -3.69   0.000    -.0504814   -.0154445
         L2. |  -.0082126   .0093613    -0.88   0.380    -.0265604    .0101351
         L3. |   .0026548   .0103783     0.26   0.798    -.0176862    .0229958
         L4. |   .0176707   .0095067     1.86   0.063     -.000962    .0363034
             |
   admin_fe4 |    .039524   .1305538     0.30   0.762    -.2163567    .2954046
   admin_fe5 |   .4820252   .2431273     1.98   0.047     .0055045    .9585459
         may |   .5750788   .1181612     4.87   0.000     .3434872    .8066704
   f_tu_elec |    .182896   .1338045     1.37   0.172     -.079356    .4451481
     tu_coup |  -.7473338   .2920141    -2.56   0.010    -1.319671   -.1749967
    gas_disp |  -.2290783   .2222373    -1.03   0.303    -.6646555    .2064989
             |
  ln_tumilex |
        L12. |   1.528958   .8853379     1.73   0.084    -.2062728    3.264188
             |
  ln_grmilex |
        L12. |   -.466648   2.859788    -0.16   0.870     -6.07173    5.138433
             |
  ln_tugdppc |
        L12. |  -1.886565   2.002458    -0.94   0.346     -5.81131     2.03818
             |
       _cons |   13.82066   15.44004     0.90   0.371    -16.44126    44.08257
-------------+----------------------------------------------------------------
approval_ip  |
   ln_airvio |
         L1. |  -1.355136   1.094333    -1.24   0.216     -3.49999    .7897184
         L2. |  -1.113189   1.071795    -1.04   0.299    -3.213868    .9874896
         L3. |  -.4861551   1.108333    -0.44   0.661    -2.658447    1.686137
         L4. |  -2.613705    1.05319    -2.48   0.013    -4.677919   -.5494915
             |
 approval_ip |
         L1. |   .3259319   .0942997     3.46   0.001     .1411079    .5107558
         L2. |   .0183631   .0987633     0.19   0.852    -.1752095    .2119357
         L3. |  -.0580828   .1094929    -0.53   0.596     -.272685    .1565194
         L4. |  -.0547786   .1002975    -0.55   0.585     -.251358    .1418008
             |
   admin_fe4 |  -1.289486   1.377372    -0.94   0.349    -3.989086    1.410113
   admin_fe5 |  -1.896047   2.565048    -0.74   0.460    -6.923448    3.131355
         may |  -.7713135   1.246627    -0.62   0.536    -3.214658    1.672031
   f_tu_elec |   -1.13167   1.411668    -0.80   0.423    -3.898488    1.635149
     tu_coup |   21.98148   3.080815     7.13   0.000     15.94319    28.01977
    gas_disp |  -2.332059   2.344655    -0.99   0.320    -6.927497     2.26338
             |
  ln_tumilex |
        L12. |    13.3952   9.340517     1.43   0.152     -4.91188    31.70227
             |
  ln_grmilex |
        L12. |   25.20856   30.17142     0.84   0.403    -33.92633    84.34345
             |
  ln_tugdppc |
        L12. |  -15.53917   21.12639    -0.74   0.462    -56.94612    25.86779
             |
       _cons |  -127.3089   162.8959    -0.78   0.434    -446.5791    191.9612
------------------------------------------------------------------------------
Estimating short-run parameters

Iteration 0:   log likelihood = -398.57108  
Iteration 1:   log likelihood = -220.86471  
Iteration 2:   log likelihood = -192.59324  
Iteration 3:   log likelihood = -191.92911  
Iteration 4:   log likelihood = -191.92574  
Iteration 5:   log likelihood = -191.92574  

Structural vector autoregression

 ( 1)  [a_1_1]_cons = 1
 ( 2)  [a_1_2]_cons = 0
 ( 3)  [a_2_2]_cons = 1
 ( 4)  [b_1_2]_cons = 0
 ( 5)  [b_2_1]_cons = 0

Sample:  2014m1 - 2020m5                        Number of obs     =         77
Exactly identified model                        Log likelihood    =  -191.9257

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      /a_1_1 |          1  (constrained)
      /a_2_1 |   2.778495   1.159866     2.40   0.017     .5051995    5.051791
      /a_1_2 |          0  (constrained)
      /a_2_2 |          1  (constrained)
-------------+----------------------------------------------------------------
      /b_1_1 |   .2637454   .0212532    12.41   0.000     .2220899    .3054009
      /b_2_1 |          0  (constrained)
      /b_1_2 |          0  (constrained)
      /b_2_2 |   2.684343   .2163106    12.41   0.000     2.260383    3.108304
------------------------------------------------------------------------------

. irf create order1, set(var2.irf) replace step(12)
(file var2.irf now active)
(file var2.irf updated)

. irf graph sirf, xlabel(0(2)12) irf(order1) impulse(approval_ip) response(ln_airvio) ///
> yline(0,lcolor(black)) scheme(plotplain) level(90) byopts(note("") legend(off)) ///
> subtitle(" ", fcolor(none) lstyle(none)) xtitle("Months after Approval Shock")  ///
> name(one, replace) ytitle("Airspace Violations Response")

. irf graph sirf, xlabel(0(2)12) irf(order1) impulse(ln_airvio) response(approval_ip) ///
> yline(0,lcolor(black)) scheme(plotplain) level(90) byopts(note("") legend(off)) ///
> subtitle(" ", fcolor(none) lstyle(none)) xtitle("Months after Airspace Violations Shock")  ///
> name(two, replace) ytitle("Approval Response")

. svar  approval_ip ln_airvio if modate>tm(2013m8), exog(admin_fe4 admin_fe5 may f_tu_elec tu_coup gas_disp l12.ln_tumilex l12.ln_grmilex l12.ln_tug
> dppc) ///
> lags(1) aeq(A1) beq(B1) var

Vector autoregression

Sample:  2013m10 - 2020m5                       Number of obs     =         80
Log likelihood =  -211.8886                     AIC               =   5.897214
FPE            =   1.253603                     HQIC              =   6.183721
Det(Sigma_ml)  =   .6848604                     SBIC              =   6.611822

Equation           Parms      RMSE     R-sq      chi2     P>chi2
----------------------------------------------------------------
approval_ip          12     3.13383   0.6224   131.8606   0.0000
ln_airvio            12     .315985   0.6346   138.9227   0.0000
----------------------------------------------------------------

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
approval_ip  |
 approval_ip |
         L1. |   .3830308   .0923063     4.15   0.000     .2021137    .5639478
             |
   ln_airvio |
         L1. |  -.7248289   .9626499    -0.75   0.451    -2.611588     1.16193
             |
   admin_fe4 |  -1.611283   1.406986    -1.15   0.252    -4.368925     1.14636
   admin_fe5 |  -4.030119   2.459466    -1.64   0.101    -8.850583    .7903453
         may |  -.6472647   1.230798    -0.53   0.599    -3.059584    1.765054
   f_tu_elec |  -1.098664   1.444376    -0.76   0.447     -3.92959    1.732261
     tu_coup |   21.24106   3.006613     7.06   0.000      15.3482    27.13391
    gas_disp |  -.8549833   2.041968    -0.42   0.675    -4.857166      3.1472
             |
  ln_tumilex |
        L12. |   1.501343   7.951105     0.19   0.850    -14.08254    17.08522
             |
  ln_grmilex |
        L12. |   31.60938   17.83824     1.77   0.076    -3.352917    66.57168
             |
  ln_tugdppc |
        L12. |   2.228522   15.53246     0.14   0.886    -28.21454    32.67158
             |
       _cons |  -273.3925   149.4074    -1.83   0.067    -566.2256    19.44067
-------------+----------------------------------------------------------------
ln_airvio    |
 approval_ip |
         L1. |  -.0284231   .0093073    -3.05   0.002     -.046665   -.0101811
             |
   ln_airvio |
         L1. |   .1897679   .0970644     1.96   0.051    -.0004748    .3800106
             |
   admin_fe4 |  -.0028288    .141867    -0.02   0.984     -.280883    .2752255
   admin_fe5 |   .3025964   .2479889     1.22   0.222    -.1834529    .7886458
         may |   .5557465   .1241018     4.48   0.000     .3125114    .7989816
   f_tu_elec |   .1542638   .1456371     1.06   0.289    -.1311796    .4397072
     tu_coup |  -.9119225    .303158    -3.01   0.003    -1.506101   -.3177437
    gas_disp |  -.1697917   .2058924    -0.82   0.410    -.5733334      .23375
             |
  ln_tumilex |
        L12. |   .7573055   .8017132     0.94   0.345    -.8140235    2.328635
             |
  ln_grmilex |
        L12. |   .0229253   1.798637     0.01   0.990    -3.502338    3.548188
             |
  ln_tugdppc |
        L12. |  -.1033197   1.566144    -0.07   0.947    -3.172906    2.966267
             |
       _cons |  -1.365894   15.06481    -0.09   0.928    -30.89238    28.16059
------------------------------------------------------------------------------
Estimating short-run parameters

Iteration 0:   log likelihood = -749.95676  
Iteration 1:   log likelihood = -693.33306  
Iteration 2:   log likelihood = -478.09153  
Iteration 3:   log likelihood = -283.12712  
Iteration 4:   log likelihood = -246.51025  
Iteration 5:   log likelihood = -212.34963  
Iteration 6:   log likelihood = -211.89077  
Iteration 7:   log likelihood = -211.88855  
Iteration 8:   log likelihood = -211.88855  

Structural vector autoregression

 ( 1)  [a_1_1]_cons = 1
 ( 2)  [a_1_2]_cons = 0
 ( 3)  [a_2_2]_cons = 1
 ( 4)  [b_1_2]_cons = 0
 ( 5)  [b_2_1]_cons = 0

Sample:  2013m10 - 2020m5                       Number of obs     =         80
Exactly identified model                        Log likelihood    =  -211.8886

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      /a_1_1 |          1  (constrained)
      /a_2_1 |   .0184069   .0110837     1.66   0.097    -.0033169    .0401306
      /a_1_2 |          0  (constrained)
      /a_2_2 |          1  (constrained)
-------------+----------------------------------------------------------------
      /b_1_1 |   2.889247    .228415    12.65   0.000     2.441562    3.336932
      /b_2_1 |          0  (constrained)
      /b_1_2 |          0  (constrained)
      /b_2_2 |   .2864286   .0226442    12.65   0.000     .2420468    .3308103
------------------------------------------------------------------------------

. irf create order1, set(var2.irf) replace step(12)
(file var2.irf now active)
(file var2.irf updated)

. irf graph sirf, xlabel(0(2)12) irf(order1) impulse(approval_ip) response(ln_airvio) ///
> yline(0,lcolor(black)) scheme(plotplain) level(90) byopts(note("") legend(off)) ///
> subtitle(" ", fcolor(none) lstyle(none)) xtitle("Months after Approval Shock")  ///
> name(three, replace) ytitle("Airspace Violations Response")

. irf graph sirf, xlabel(0(2)12) irf(order1) impulse(ln_airvio) response(approval_ip) ///
> yline(0,lcolor(black)) scheme(plotplain) level(90) byopts(note("") legend(off)) ///
> subtitle(" ", fcolor(none) lstyle(none)) xtitle("Months after Airspace Violations Shock")  ///
> name(four, replace) ytitle("Approval Response")

. graph combine one two three four, ycommon scheme(plotplain)

. 
. log close
      name:  <unnamed>
       log:  C:\Users\mariu\Box\myBox\Air Space Violations\Approval and Airspace Violations\Data\FPA Replication\FPA_ReplicationLog.log
  log type:  text
 closed on:  28 Dec 2020, 12:01:23
----------------------------------------------------------------------------------------------------------------------------------------------------
